| Package | Description |
|---|---|
| org.bytedeco.pytorch | |
| org.bytedeco.pytorch.global |
| Modifier and Type | Method and Description |
|---|---|
Tensor |
Tensor.__and__(Scalar other) |
Tensor |
Tensor.__and__(Tensor other) |
Tensor |
Tensor.__dispatch_conj() |
Tensor |
Tensor.__dispatch_contiguous() |
Tensor |
Tensor.__dispatch_contiguous(torch.MemoryFormat memory_format) |
Tensor |
Tensor.__dispatch_data() |
Tensor |
Tensor.__dispatch_requires_grad_() |
Tensor |
Tensor.__dispatch_requires_grad_(boolean requires_grad) |
Tensor |
Tensor.__iand__(Scalar other) |
Tensor |
Tensor.__iand__(Tensor other) |
Tensor |
Tensor.__ilshift__(Scalar other) |
Tensor |
Tensor.__ilshift__(Tensor other) |
Tensor |
Tensor.__ior__(Scalar other) |
Tensor |
Tensor.__ior__(Tensor other) |
Tensor |
Tensor.__irshift__(Scalar other) |
Tensor |
Tensor.__irshift__(Tensor other) |
Tensor |
Tensor.__ixor__(Scalar other) |
Tensor |
Tensor.__ixor__(Tensor other) |
Tensor |
Tensor.__lshift__(Scalar other) |
Tensor |
Tensor.__lshift__(Tensor other) |
Tensor |
Tensor.__or__(Scalar other) |
Tensor |
Tensor.__or__(Tensor other) |
Tensor |
Tensor.__rshift__(Scalar other) |
Tensor |
Tensor.__rshift__(Tensor other) |
Tensor |
Tensor.__xor__(Scalar other) |
Tensor |
Tensor.__xor__(Tensor other) |
Tensor |
Tensor._coalesced_(boolean coalesced) |
Tensor |
Tensor._conj_physical() |
Tensor |
Tensor._conj() |
Tensor |
Tensor._fw_grad(long level)
This function returns the forward gradient for this Tensor at the given level.
|
Tensor |
TensorImpl._fw_grad(long level,
TensorBase self)
Return the accumulated gradient of a tensor.
|
Tensor |
Tensor._fw_primal(long level) |
Tensor |
AdaptiveLogSoftmaxWithLossImpl._get_full_log_prob(Tensor input,
Tensor head_output)
Given input tensor, and output of
head, computes the log of the full distribution |
Tensor |
Tensor._indices() |
Tensor |
Tensor._neg_view() |
Tensor |
FractionalMaxPool3dOptions._random_samples() |
Tensor |
FractionalMaxPool3dImpl._random_samples() |
Tensor |
FractionalMaxPool2dOptions._random_samples() |
Tensor |
FractionalMaxPool2dImpl._random_samples() |
Tensor |
FractionalMaxPool1dOptions._random_samples() |
Tensor |
Tensor._reshape_alias(long[] size,
long... stride) |
Tensor |
Tensor._reshape_alias(LongArrayRef size,
LongArrayRef stride) |
Tensor |
Tensor._values() |
Tensor |
EmbeddingOptions._weight() |
Tensor |
EmbeddingBagOptions._weight() |
Tensor |
Tensor.abs_() |
Tensor |
Tensor.abs() |
Tensor |
Tensor.absolute_() |
Tensor |
Tensor.absolute() |
Tensor |
parameter_iterator.access() |
Tensor |
buffer_iterator.access() |
Tensor |
TensorMaybeOwned.access() |
Tensor |
TensorArg.access() |
Tensor |
OptionalTensorRef.access() |
Tensor |
Tensor.acos_() |
Tensor |
Tensor.acos() |
Tensor |
Tensor.acosh_() |
Tensor |
Tensor.acosh() |
Tensor |
Tensor.add_(Scalar other) |
Tensor |
Tensor.add_(Scalar other,
Scalar alpha) |
Tensor |
Tensor.add_(Tensor other) |
Tensor |
Tensor.add_(Tensor other,
Scalar alpha) |
Tensor |
Tensor.add(Scalar other) |
Tensor |
Tensor.add(Scalar other,
Scalar alpha) |
Tensor |
Tensor.add(Tensor other) |
Tensor |
Tensor.add(Tensor other,
Scalar alpha) |
Tensor |
Tensor.addbmm_(Tensor batch1,
Tensor batch2) |
Tensor |
Tensor.addbmm_(Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addbmm(Tensor batch1,
Tensor batch2) |
Tensor |
Tensor.addbmm(Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addcdiv_(Tensor tensor1,
Tensor tensor2) |
Tensor |
Tensor.addcdiv_(Tensor tensor1,
Tensor tensor2,
Scalar value) |
Tensor |
Tensor.addcdiv(Tensor tensor1,
Tensor tensor2) |
Tensor |
Tensor.addcdiv(Tensor tensor1,
Tensor tensor2,
Scalar value) |
Tensor |
Tensor.addcmul_(Tensor tensor1,
Tensor tensor2) |
Tensor |
Tensor.addcmul_(Tensor tensor1,
Tensor tensor2,
Scalar value) |
Tensor |
Tensor.addcmul(Tensor tensor1,
Tensor tensor2) |
Tensor |
Tensor.addcmul(Tensor tensor1,
Tensor tensor2,
Scalar value) |
Tensor |
Tensor.addmm_(Tensor mat1,
Tensor mat2) |
Tensor |
Tensor.addmm_(Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addmm(Tensor mat1,
Tensor mat2) |
Tensor |
Tensor.addmm(Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addmv_(Tensor mat,
Tensor vec) |
Tensor |
Tensor.addmv_(Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addmv(Tensor mat,
Tensor vec) |
Tensor |
Tensor.addmv(Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addPut(Scalar other) |
Tensor |
Tensor.addPut(Tensor other) |
Tensor |
Tensor.addr_(Tensor vec1,
Tensor vec2) |
Tensor |
Tensor.addr_(Tensor vec1,
Tensor vec2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addr(Tensor vec1,
Tensor vec2) |
Tensor |
Tensor.addr(Tensor vec1,
Tensor vec2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.alias() |
Tensor |
Tensor.align_as(Tensor other) |
Tensor |
Tensor.align_to(DimnameArrayRef names) |
Tensor |
Tensor.align_to(DimnameArrayRef order,
long ellipsis_idx) |
Tensor |
Tensor.all() |
Tensor |
Tensor.all(Dimname dim) |
Tensor |
Tensor.all(Dimname dim,
boolean keepdim) |
Tensor |
Tensor.all(long dim) |
Tensor |
Tensor.all(long dim,
boolean keepdim) |
Tensor |
Tensor.amax() |
Tensor |
Tensor.amax(long[] dim,
boolean keepdim) |
Tensor |
Tensor.amax(LongArrayRef dim,
boolean keepdim) |
Tensor |
Tensor.amin() |
Tensor |
Tensor.amin(long[] dim,
boolean keepdim) |
Tensor |
Tensor.amin(LongArrayRef dim,
boolean keepdim) |
Tensor |
Tensor.andPut(Tensor other) |
Tensor |
Tensor.angle() |
Tensor |
Tensor.any() |
Tensor |
Tensor.any(Dimname dim) |
Tensor |
Tensor.any(Dimname dim,
boolean keepdim) |
Tensor |
Tensor.any(long dim) |
Tensor |
Tensor.any(long dim,
boolean keepdim) |
Tensor |
Tensor.arccos_() |
Tensor |
Tensor.arccos() |
Tensor |
Tensor.arccosh_() |
Tensor |
Tensor.arccosh() |
Tensor |
Tensor.arcsin_() |
Tensor |
Tensor.arcsin() |
Tensor |
Tensor.arcsinh_() |
Tensor |
Tensor.arcsinh() |
Tensor |
Tensor.arctan_() |
Tensor |
Tensor.arctan() |
Tensor |
Tensor.arctanh_() |
Tensor |
Tensor.arctanh() |
Tensor |
Tensor.argmax() |
Tensor |
Tensor.argmax(LongOptional dim,
boolean keepdim) |
Tensor |
Tensor.argmin() |
Tensor |
Tensor.argmin(LongOptional dim,
boolean keepdim) |
Tensor |
Tensor.argsort() |
Tensor |
Tensor.argsort(Dimname dim) |
Tensor |
Tensor.argsort(Dimname dim,
boolean descending) |
Tensor |
Tensor.argsort(long dim,
boolean descending) |
Tensor |
Tensor.as_strided_(long[] size,
long... stride) |
Tensor |
Tensor.as_strided_(long[] size,
long[] stride,
LongOptional storage_offset) |
Tensor |
Tensor.as_strided_(LongArrayRef size,
LongArrayRef stride) |
Tensor |
Tensor.as_strided_(LongArrayRef size,
LongArrayRef stride,
LongOptional storage_offset) |
Tensor |
Tensor.as_strided(long[] size,
long... stride) |
Tensor |
Tensor.as_strided(long[] size,
long[] stride,
LongOptional storage_offset) |
Tensor |
Tensor.as_strided(LongArrayRef size,
LongArrayRef stride) |
Tensor |
Tensor.as_strided(LongArrayRef size,
LongArrayRef stride,
LongOptional storage_offset) |
Tensor |
Tensor.asin_() |
Tensor |
Tensor.asin() |
Tensor |
Tensor.asinh_() |
Tensor |
Tensor.asinh() |
Tensor |
TensorArrayRef.at(long Index)
Vector compatibility
|
Tensor |
ParameterListImpl.at(long idx)
Returns the value associated with the given
key. |
Tensor |
Tensor.atan_() |
Tensor |
Tensor.atan() |
Tensor |
Tensor.atan2_(Tensor other) |
Tensor |
Tensor.atan2(Tensor other) |
Tensor |
Tensor.atanh_() |
Tensor |
Tensor.atanh() |
Tensor |
MultiheadAttentionForwardFuncOptions.attn_mask() |
Tensor |
TensorArrayRef.back()
back - Get the last element.
|
Tensor |
Tensor.baddbmm_(Tensor batch1,
Tensor batch2) |
Tensor |
Tensor.baddbmm_(Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.baddbmm(Tensor batch1,
Tensor batch2) |
Tensor |
Tensor.baddbmm(Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
Tensor |
PackedSequence.batch_sizes() |
Tensor |
TensorArrayRef.begin()
\}
\name Simple Operations
\{
|
Tensor |
Tensor.bernoulli_() |
Tensor |
Tensor.bernoulli_(double p,
GeneratorOptional generator) |
Tensor |
Tensor.bernoulli_(Tensor p) |
Tensor |
Tensor.bernoulli_(Tensor p,
GeneratorOptional generator) |
Tensor |
Tensor.bernoulli() |
Tensor |
Tensor.bernoulli(double p) |
Tensor |
Tensor.bernoulli(double p,
GeneratorOptional generator) |
Tensor |
Tensor.bernoulli(GeneratorOptional generator) |
Tensor |
RNNCellImplBase.bias_hh() |
Tensor |
LSTMCellImplBase.bias_hh() |
Tensor |
GRUCellImplBase.bias_hh() |
Tensor |
RNNCellImplBase.bias_ih() |
Tensor |
LSTMCellImplBase.bias_ih() |
Tensor |
GRUCellImplBase.bias_ih() |
Tensor |
MultiheadAttentionImpl.bias_k() |
Tensor |
MultiheadAttentionForwardFuncOptions.bias_k() |
Tensor |
MultiheadAttentionImpl.bias_v() |
Tensor |
MultiheadAttentionForwardFuncOptions.bias_v() |
Tensor |
LinearImpl.bias()
The learned bias.
|
Tensor |
LayerNormImpl.bias()
The learned bias.
|
Tensor |
LayerNormFuncOptions.bias() |
Tensor |
InstanceNormFuncOptions.bias() |
Tensor |
InstanceNorm3dImplBaseBase.bias()
The learned bias.
|
Tensor |
InstanceNorm2dImplBaseBase.bias()
The learned bias.
|
Tensor |
InstanceNorm1dImplBaseBase.bias()
The learned bias.
|
Tensor |
GroupNormImpl.bias()
The learned bias.
|
Tensor |
GroupNormFuncOptions.bias() |
Tensor |
ConvTranspose3dImplBaseBase.bias()
The learned bias.
|
Tensor |
ConvTranspose3dFuncOptions.bias() |
Tensor |
ConvTranspose2dImplBaseBase.bias()
The learned bias.
|
Tensor |
ConvTranspose2dFuncOptions.bias() |
Tensor |
ConvTranspose1dImplBaseBase.bias()
The learned bias.
|
Tensor |
ConvTranspose1dFuncOptions.bias() |
Tensor |
Conv3dImplBase.bias()
The learned bias.
|
Tensor |
Conv3dFuncOptions.bias() |
Tensor |
Conv2dImplBase.bias()
The learned bias.
|
Tensor |
Conv2dFuncOptions.bias() |
Tensor |
Conv1dImplBase.bias()
The learned bias.
|
Tensor |
Conv1dFuncOptions.bias() |
Tensor |
BilinearImpl.bias()
The learned bias.
|
Tensor |
BatchNormFuncOptions.bias() |
Tensor |
BatchNorm3dImplBaseBase.bias()
The learned bias.
|
Tensor |
BatchNorm2dImplBaseBase.bias()
The learned bias.
|
Tensor |
BatchNorm1dImplBaseBase.bias()
The learned bias.
|
Tensor |
Tensor.bincount() |
Tensor |
Tensor.bincount(TensorOptional weights,
long minlength) |
Tensor |
Tensor.bitwise_and_(Scalar other) |
Tensor |
Tensor.bitwise_and_(Tensor other) |
Tensor |
Tensor.bitwise_and(Scalar other) |
Tensor |
Tensor.bitwise_and(Tensor other) |
Tensor |
Tensor.bitwise_left_shift_(Scalar other) |
Tensor |
Tensor.bitwise_left_shift_(Tensor other) |
Tensor |
Tensor.bitwise_left_shift(Scalar other) |
Tensor |
Tensor.bitwise_left_shift(Tensor other) |
Tensor |
Tensor.bitwise_not_() |
Tensor |
Tensor.bitwise_not() |
Tensor |
Tensor.bitwise_or_(Scalar other) |
Tensor |
Tensor.bitwise_or_(Tensor other) |
Tensor |
Tensor.bitwise_or(Scalar other) |
Tensor |
Tensor.bitwise_or(Tensor other) |
Tensor |
Tensor.bitwise_right_shift_(Scalar other) |
Tensor |
Tensor.bitwise_right_shift_(Tensor other) |
Tensor |
Tensor.bitwise_right_shift(Scalar other) |
Tensor |
Tensor.bitwise_right_shift(Tensor other) |
Tensor |
Tensor.bitwise_xor_(Scalar other) |
Tensor |
Tensor.bitwise_xor_(Tensor other) |
Tensor |
Tensor.bitwise_xor(Scalar other) |
Tensor |
Tensor.bitwise_xor(Tensor other) |
Tensor |
Tensor.bmm(Tensor mat2) |
Tensor |
Tensor.broadcast_to(long... size) |
Tensor |
Tensor.broadcast_to(LongArrayRef size) |
Tensor |
SavedVariableHooks.call_unpack_hook() |
Tensor |
Tensor.cauchy_() |
Tensor |
Tensor.cauchy_(double median,
double sigma,
GeneratorOptional generator) |
Tensor |
TensorArrayRef.cbegin() |
Tensor |
Tensor.ceil_() |
Tensor |
Tensor.ceil() |
Tensor |
TensorArrayRef.cend() |
Tensor |
Tensor.cholesky_inverse() |
Tensor |
Tensor.cholesky_inverse(boolean upper) |
Tensor |
Tensor.cholesky_solve(Tensor input2) |
Tensor |
Tensor.cholesky_solve(Tensor input2,
boolean upper) |
Tensor |
Tensor.cholesky() |
Tensor |
Tensor.cholesky(boolean upper) |
Tensor |
Tensor.clamp_() |
Tensor |
Tensor.clamp_(ScalarOptional min) |
Tensor |
Tensor.clamp_(ScalarOptional min,
ScalarOptional max) |
Tensor |
Tensor.clamp_(TensorOptional min,
TensorOptional max) |
Tensor |
Tensor.clamp_max_(Scalar max) |
Tensor |
Tensor.clamp_max_(Tensor max) |
Tensor |
Tensor.clamp_max(Scalar max) |
Tensor |
Tensor.clamp_max(Tensor max) |
Tensor |
Tensor.clamp_min_(Scalar min) |
Tensor |
Tensor.clamp_min_(Tensor min) |
Tensor |
Tensor.clamp_min(Scalar min) |
Tensor |
Tensor.clamp_min(Tensor min) |
Tensor |
Tensor.clamp() |
Tensor |
Tensor.clamp(ScalarOptional min) |
Tensor |
Tensor.clamp(ScalarOptional min,
ScalarOptional max) |
Tensor |
Tensor.clamp(TensorOptional min,
TensorOptional max) |
Tensor |
Tensor.clip_() |
Tensor |
Tensor.clip_(ScalarOptional min) |
Tensor |
Tensor.clip_(ScalarOptional min,
ScalarOptional max) |
Tensor |
Tensor.clip_(TensorOptional min,
TensorOptional max) |
Tensor |
Tensor.clip() |
Tensor |
Tensor.clip(ScalarOptional min) |
Tensor |
Tensor.clip(ScalarOptional min,
ScalarOptional max) |
Tensor |
Tensor.clip(TensorOptional min,
TensorOptional max) |
Tensor |
Tensor.clone() |
Tensor |
Tensor.clone(MemoryFormatOptional memory_format) |
Tensor |
Tensor.coalesce() |
Tensor |
Tensor.col_indices() |
Tensor |
Tensor.conj_physical_() |
Tensor |
Tensor.conj_physical() |
Tensor |
Tensor.conj() |
Tensor |
Tensor.contiguous() |
Tensor |
Tensor.contiguous(byte memory_format) |
Tensor |
Tensor.contiguous(torch.MemoryFormat memory_format) |
Tensor |
Tensor.copy_(Tensor src) |
Tensor |
Tensor.copy_(Tensor src,
boolean non_blocking) |
Tensor |
Tensor.copysign_(Scalar other) |
Tensor |
Tensor.copysign_(Tensor other) |
Tensor |
Tensor.copysign(Scalar other) |
Tensor |
Tensor.copysign(Tensor other) |
Tensor |
Tensor.corrcoef() |
Tensor |
Tensor.cos_() |
Tensor |
Tensor.cos() |
Tensor |
Tensor.cosh_() |
Tensor |
Tensor.cosh() |
Tensor |
Tensor.count_nonzero() |
Tensor |
Tensor.count_nonzero(long... dim) |
Tensor |
Tensor.count_nonzero(LongArrayRef dim) |
Tensor |
Tensor.count_nonzero(LongOptional dim) |
Tensor |
Tensor.cov() |
Tensor |
Tensor.cov(long correction,
TensorOptional fweights,
TensorOptional aweights) |
Tensor |
Tensor.cpu() |
static Tensor |
AbstractTensor.create(boolean... data) |
static Tensor |
AbstractTensor.create(boolean[] data,
long... shape) |
static Tensor |
AbstractTensor.create(byte... data) |
static Tensor |
AbstractTensor.create(byte[] data,
boolean signed) |
static Tensor |
AbstractTensor.create(byte[] data,
boolean signed,
long... shape) |
static Tensor |
AbstractTensor.create(byte[] data,
long... shape) |
static Tensor |
AbstractTensor.create(double... data) |
static Tensor |
AbstractTensor.create(double[] data,
long... shape) |
static Tensor |
AbstractTensor.create(float... data) |
static Tensor |
AbstractTensor.create(float[] data,
long... shape) |
static Tensor |
AbstractTensor.create(int... data) |
static Tensor |
AbstractTensor.create(int[] data,
long... shape) |
static Tensor |
AbstractTensor.create(long... data) |
static Tensor |
AbstractTensor.create(long[] data,
long... shape) |
static Tensor |
AbstractTensor.create(short... data) |
static Tensor |
AbstractTensor.create(short[] data,
long... shape) |
static Tensor |
ParameterPolicy.create(SlotCursor cursors,
IValue v) |
static Tensor |
BufferPolicy.create(SlotCursor cursors,
IValue v) |
Tensor |
Tensor.cross(Tensor other) |
Tensor |
Tensor.cross(Tensor other,
LongOptional dim) |
Tensor |
Tensor.crow_indices() |
Tensor |
Tensor.cuda() |
Tensor |
Tensor.cumprod_(Dimname dim) |
Tensor |
Tensor.cumprod_(Dimname dim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.cumprod_(long dim) |
Tensor |
Tensor.cumprod_(long dim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.cumprod(Dimname dim) |
Tensor |
Tensor.cumprod(Dimname dim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.cumprod(long dim) |
Tensor |
Tensor.cumprod(long dim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.cumsum_(Dimname dim) |
Tensor |
Tensor.cumsum_(Dimname dim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.cumsum_(long dim) |
Tensor |
Tensor.cumsum_(long dim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.cumsum(Dimname dim) |
Tensor |
Tensor.cumsum(Dimname dim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.cumsum(long dim) |
Tensor |
Tensor.cumsum(long dim,
ScalarTypeOptional dtype) |
Tensor |
LBFGSParamState.d() |
Tensor |
TensorArrayRef.data() |
Tensor |
Tensor.data()
Registers a backward hook.
|
Tensor |
PackedSequence.data() |
Tensor |
Example.data() |
Tensor |
Tensor.deg2rad_() |
Tensor |
Tensor.deg2rad() |
Tensor |
Tensor.dequantize() |
Tensor |
Tensor.det() |
Tensor |
Tensor.detach_() |
Tensor |
Tensor.detach() |
Tensor |
Tensor.diag_embed() |
Tensor |
Tensor.diag_embed(long offset,
long dim1,
long dim2) |
Tensor |
Tensor.diag() |
Tensor |
Tensor.diag(long diagonal) |
Tensor |
Tensor.diagflat() |
Tensor |
Tensor.diagflat(long offset) |
Tensor |
Tensor.diagonal() |
Tensor |
Tensor.diagonal(Dimname outdim,
Dimname dim1,
Dimname dim2) |
Tensor |
Tensor.diagonal(Dimname outdim,
Dimname dim1,
Dimname dim2,
long offset) |
Tensor |
Tensor.diagonal(long offset,
long dim1,
long dim2) |
Tensor |
Tensor.diff() |
Tensor |
Tensor.diff(long n,
long dim,
TensorOptional prepend,
TensorOptional append) |
Tensor |
Tensor.digamma_() |
Tensor |
Tensor.digamma() |
Tensor |
Tensor.dist(Tensor other) |
Tensor |
Tensor.dist(Tensor other,
Scalar p) |
Tensor |
Tensor.div_(Scalar other) |
Tensor |
Tensor.div_(Scalar other,
Pointer rounding_mode) |
Tensor |
Tensor.div_(Tensor other) |
Tensor |
Tensor.div_(Tensor other,
Pointer rounding_mode) |
Tensor |
Tensor.div(Scalar other) |
Tensor |
Tensor.div(Scalar other,
Pointer rounding_mode) |
Tensor |
Tensor.div(Tensor other) |
Tensor |
Tensor.div(Tensor other,
Pointer rounding_mode) |
Tensor |
Tensor.divide_(Scalar other) |
Tensor |
Tensor.divide_(Scalar other,
Pointer rounding_mode) |
Tensor |
Tensor.divide_(Tensor other) |
Tensor |
Tensor.divide_(Tensor other,
Pointer rounding_mode) |
Tensor |
Tensor.divide(Scalar other) |
Tensor |
Tensor.divide(Scalar other,
Pointer rounding_mode) |
Tensor |
Tensor.divide(Tensor other) |
Tensor |
Tensor.divide(Tensor other,
Pointer rounding_mode) |
Tensor |
Tensor.dividePut(Scalar other) |
Tensor |
Tensor.dividePut(Tensor other) |
Tensor |
Tensor.dot(Tensor tensor) |
Tensor |
TensorArrayRef.end() |
Tensor |
Tensor.eq_(Scalar other) |
Tensor |
Tensor.eq_(Tensor other) |
Tensor |
Tensor.eq(Scalar other) |
Tensor |
Tensor.eq(Tensor other) |
Tensor |
Tensor.erf_() |
Tensor |
Tensor.erf() |
Tensor |
Tensor.erfc_() |
Tensor |
Tensor.erfc() |
Tensor |
Tensor.erfinv_() |
Tensor |
Tensor.erfinv() |
Tensor |
Tensor.exp_() |
Tensor |
AdamWParamState.exp_avg_sq() |
Tensor |
AdamParamState.exp_avg_sq() |
Tensor |
AdamWParamState.exp_avg() |
Tensor |
AdamParamState.exp_avg() |
Tensor |
Tensor.exp() |
Tensor |
Tensor.exp2_() |
Tensor |
Tensor.exp2() |
Tensor |
Tensor.expand_as(Tensor other) |
Tensor |
Tensor.expand(long... size) |
Tensor |
Tensor.expand(long[] size,
boolean implicit) |
Tensor |
Tensor.expand(LongArrayRef size) |
Tensor |
Tensor.expand(LongArrayRef size,
boolean implicit) |
Tensor |
Tensor.expm1_() |
Tensor |
Tensor.expm1() |
Tensor |
Tensor.exponential_() |
Tensor |
Tensor.exponential_(double lambd,
GeneratorOptional generator) |
Tensor |
Tensor.fill_(Scalar value) |
Tensor |
Tensor.fill_(Tensor value) |
Tensor |
Tensor.fill_diagonal_(Scalar fill_value) |
Tensor |
Tensor.fill_diagonal_(Scalar fill_value,
boolean wrap) |
Tensor |
Tensor.fix_() |
Tensor |
Tensor.fix() |
Tensor |
Tensor.flatten() |
Tensor |
Tensor.flatten(DimnameArrayRef dims,
Dimname out_dim) |
Tensor |
Tensor.flatten(Dimname start_dim,
Dimname end_dim,
Dimname out_dim) |
Tensor |
Tensor.flatten(long start_dim,
long end_dim) |
Tensor |
Tensor.flatten(long start_dim,
long end_dim,
Dimname out_dim) |
Tensor |
Tensor.flip(long... dims) |
Tensor |
Tensor.flip(LongArrayRef dims) |
Tensor |
Tensor.fliplr() |
Tensor |
Tensor.flipud() |
Tensor |
Tensor.float_power_(Scalar exponent) |
Tensor |
Tensor.float_power_(Tensor exponent) |
Tensor |
Tensor.float_power(Scalar exponent) |
Tensor |
Tensor.float_power(Tensor exponent) |
Tensor |
Tensor.floor_() |
Tensor |
Tensor.floor_divide_(Scalar other) |
Tensor |
Tensor.floor_divide_(Tensor other) |
Tensor |
Tensor.floor_divide(Scalar other) |
Tensor |
Tensor.floor_divide(Tensor other) |
Tensor |
Tensor.floor() |
Tensor |
Tensor.fmax(Tensor other) |
Tensor |
Tensor.fmin(Tensor other) |
Tensor |
Tensor.fmod_(Scalar other) |
Tensor |
Tensor.fmod_(Tensor other) |
Tensor |
Tensor.fmod(Scalar other) |
Tensor |
Tensor.fmod(Tensor other) |
Tensor |
ZeroPad2dImpl.forward(Tensor input) |
Tensor |
UpsampleImpl.forward(Tensor input) |
Tensor |
UnfoldImpl.forward(Tensor input) |
Tensor |
UnflattenImpl.forward(Tensor input)
Applies an unflatten transform on the
input. |
Tensor |
TransformerEncoderLayerImpl.forward(Tensor src) |
Tensor |
TransformerEncoderImpl.forward(Tensor src) |
Tensor |
ThresholdImpl.forward(Tensor input) |
Tensor |
TanhshrinkImpl.forward(Tensor input) |
Tensor |
TanhImpl.forward(Tensor input) |
Tensor |
SoftsignImpl.forward(Tensor input) |
Tensor |
SoftshrinkImpl.forward(Tensor input) |
Tensor |
SoftplusImpl.forward(Tensor input) |
Tensor |
SoftminImpl.forward(Tensor input) |
Tensor |
SoftmaxImpl.forward(Tensor input) |
Tensor |
Softmax2dImpl.forward(Tensor input) |
Tensor |
SigmoidImpl.forward(Tensor input) |
Tensor |
SiLUImpl.forward(Tensor input) |
Tensor |
SELUImpl.forward(Tensor input) |
Tensor |
ReplicationPad3dImplBase.forward(Tensor input) |
Tensor |
ReplicationPad2dImplBase.forward(Tensor input) |
Tensor |
ReplicationPad1dImplBase.forward(Tensor input) |
Tensor |
ReflectionPad3dImplBase.forward(Tensor input) |
Tensor |
ReflectionPad2dImplBase.forward(Tensor input) |
Tensor |
ReflectionPad1dImplBase.forward(Tensor input) |
Tensor |
ReLUImpl.forward(Tensor input) |
Tensor |
ReLU6Impl.forward(Tensor input) |
Tensor |
RReLUImpl.forward(Tensor input) |
Tensor |
RNNCellImpl.forward(Tensor input) |
Tensor |
PixelUnshuffleImpl.forward(Tensor input) |
Tensor |
PixelShuffleImpl.forward(Tensor input) |
Tensor |
PReLUImpl.forward(Tensor input) |
Tensor |
MishImpl.forward(Tensor input) |
Tensor |
MaxPool3dImpl.forward(Tensor input) |
Tensor |
MaxPool2dImpl.forward(Tensor input) |
Tensor |
MaxPool1dImpl.forward(Tensor input) |
Tensor |
LogSoftmaxImpl.forward(Tensor input) |
Tensor |
LogSigmoidImpl.forward(Tensor input) |
Tensor |
LocalResponseNormImpl.forward(Tensor input) |
Tensor |
LinearImpl.forward(Tensor input)
Transforms the
input tensor by multiplying with the weight and
optionally adding the bias, if with_bias is true in the options. |
Tensor |
LeakyReLUImpl.forward(Tensor input) |
Tensor |
LayerNormImpl.forward(Tensor input)
Applies layer normalization over a mini-batch of inputs as described in
the paper
Layer Normalization_ . |
Tensor |
LPPool2dImpl.forward(Tensor input) |
Tensor |
LPPool1dImpl.forward(Tensor input) |
Tensor |
InstanceNorm3dImplBase.forward(Tensor input) |
Tensor |
InstanceNorm2dImplBase.forward(Tensor input) |
Tensor |
InstanceNorm1dImplBase.forward(Tensor input) |
Tensor |
IdentityImpl.forward(Tensor input) |
Tensor |
HardtanhImpl.forward(Tensor input) |
Tensor |
HardshrinkImpl.forward(Tensor input) |
Tensor |
GroupNormImpl.forward(Tensor input) |
Tensor |
GRUCellImpl.forward(Tensor input) |
Tensor |
GLUImpl.forward(Tensor input) |
Tensor |
GELUImpl.forward(Tensor input) |
Tensor |
FractionalMaxPool3dImpl.forward(Tensor input) |
Tensor |
FractionalMaxPool2dImpl.forward(Tensor input) |
Tensor |
FoldImpl.forward(Tensor input) |
Tensor |
FlattenImpl.forward(Tensor input)
Applies a flatten transform on the
input. |
Tensor |
FeatureAlphaDropoutImpl.forward(Tensor input) |
Tensor |
EmbeddingImpl.forward(Tensor indices)
Performs a lookup on the embedding table stored in
weight using the
indices supplied and returns the result. |
Tensor |
EmbeddingBagImpl.forward(Tensor input) |
Tensor |
ELUImpl.forward(Tensor input) |
Tensor |
DropoutImpl.forward(Tensor input) |
Tensor |
Dropout3dImpl.forward(Tensor input) |
Tensor |
Dropout2dImpl.forward(Tensor input) |
Tensor |
CrossMapLRN2dImpl.forward(Tensor input) |
Tensor |
ConvTranspose3dImpl.forward(Tensor input) |
Tensor |
ConvTranspose2dImpl.forward(Tensor input) |
Tensor |
ConvTranspose1dImpl.forward(Tensor input) |
Tensor |
Conv3dImpl.forward(Tensor input) |
Tensor |
Conv2dImpl.forward(Tensor input) |
Tensor |
Conv1dImpl.forward(Tensor input) |
Tensor |
ConstantPad3dImplBase.forward(Tensor input) |
Tensor |
ConstantPad2dImplBase.forward(Tensor input) |
Tensor |
ConstantPad1dImplBase.forward(Tensor input) |
Tensor |
CELUImpl.forward(Tensor input) |
Tensor |
BatchNorm3dImplBase.forward(Tensor input) |
Tensor |
BatchNorm2dImplBase.forward(Tensor input) |
Tensor |
BatchNorm1dImplBase.forward(Tensor input) |
Tensor |
AvgPool3dImpl.forward(Tensor input) |
Tensor |
AvgPool2dImpl.forward(Tensor input) |
Tensor |
AvgPool1dImpl.forward(Tensor input) |
Tensor |
AlphaDropoutImpl.forward(Tensor input) |
Tensor |
AdaptiveMaxPool3dImpl.forward(Tensor input) |
Tensor |
AdaptiveMaxPool2dImpl.forward(Tensor input) |
Tensor |
AdaptiveMaxPool1dImpl.forward(Tensor input) |
Tensor |
AdaptiveAvgPool3dImpl.forward(Tensor input) |
Tensor |
AdaptiveAvgPool2dImpl.forward(Tensor input) |
Tensor |
AdaptiveAvgPool1dImpl.forward(Tensor input) |
Tensor |
ConvTranspose3dImpl.forward(Tensor input,
LongArrayRefOptional output_size) |
Tensor |
ConvTranspose2dImpl.forward(Tensor input,
LongArrayRefOptional output_size) |
Tensor |
ConvTranspose1dImpl.forward(Tensor input,
LongArrayRefOptional output_size) |
Tensor |
TransformerImpl.forward(Tensor src,
Tensor tgt) |
Tensor |
TransformerDecoderLayerImpl.forward(Tensor tgt,
Tensor memory) |
Tensor |
TransformerDecoderImpl.forward(Tensor tgt,
Tensor memory) |
Tensor |
SoftMarginLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
SmoothL1LossImpl.forward(Tensor input,
Tensor target) |
Tensor |
RNNCellImpl.forward(Tensor input,
Tensor hx) |
Tensor |
PoissonNLLLossImpl.forward(Tensor log_input,
Tensor targets) |
Tensor |
PairwiseDistanceImpl.forward(Tensor input1,
Tensor input2) |
Tensor |
NLLLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
MultiMarginLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
MultiLabelSoftMarginLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
MultiLabelMarginLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
MaxUnpool3dImpl.forward(Tensor input,
Tensor indices) |
Tensor |
MaxUnpool2dImpl.forward(Tensor input,
Tensor indices) |
Tensor |
MaxUnpool1dImpl.forward(Tensor input,
Tensor indices) |
Tensor |
MSELossImpl.forward(Tensor input,
Tensor target) |
Tensor |
L1LossImpl.forward(Tensor input,
Tensor target) |
Tensor |
KLDivLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
HuberLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
HingeEmbeddingLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
GRUCellImpl.forward(Tensor input,
Tensor hx) |
Tensor |
CrossEntropyLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
CosineSimilarityImpl.forward(Tensor input1,
Tensor input2) |
Tensor |
BilinearImpl.forward(Tensor input1,
Tensor input2)
Applies a bilinear transform on the
input1 and input2 tensor by multiplying
with the weight and optionally adding the bias, if with_bias
is true in the options. |
Tensor |
BCEWithLogitsLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
BCELossImpl.forward(Tensor input,
Tensor target) |
Tensor |
MaxUnpool3dImpl.forward(Tensor input,
Tensor indices,
LongVectorOptional output_size) |
Tensor |
MaxUnpool2dImpl.forward(Tensor input,
Tensor indices,
LongVectorOptional output_size) |
Tensor |
MaxUnpool1dImpl.forward(Tensor input,
Tensor indices,
LongVectorOptional output_size) |
Tensor |
TripletMarginWithDistanceLossImpl.forward(Tensor anchor,
Tensor positive,
Tensor negative) |
Tensor |
TripletMarginLossImpl.forward(Tensor anchor,
Tensor positive,
Tensor negative) |
Tensor |
TransformerEncoderLayerImpl.forward(Tensor src,
Tensor src_mask,
Tensor src_key_padding_mask) |
Tensor |
TransformerEncoderImpl.forward(Tensor src,
Tensor src_mask,
Tensor src_key_padding_mask) |
Tensor |
MarginRankingLossImpl.forward(Tensor input1,
Tensor input2,
Tensor targets) |
Tensor |
EmbeddingBagImpl.forward(Tensor input,
Tensor offsets,
Tensor per_sample_weights) |
Tensor |
CosineEmbeddingLossImpl.forward(Tensor input1,
Tensor input2,
Tensor target) |
Tensor |
CTCLossImpl.forward(Tensor log_probs,
Tensor targets,
Tensor input_lengths,
Tensor target_lengths) |
Tensor |
TransformerDecoderLayerImpl.forward(Tensor tgt,
Tensor memory,
Tensor tgt_mask,
Tensor memory_mask,
Tensor tgt_key_padding_mask,
Tensor memory_key_padding_mask)
Pass the inputs (and mask) through the decoder layer.
|
Tensor |
TransformerDecoderImpl.forward(Tensor tgt,
Tensor memory,
Tensor tgt_mask,
Tensor memory_mask,
Tensor tgt_key_padding_mask,
Tensor memory_key_padding_mask)
Pass the inputs (and mask) through the decoder layer in turn.
|
Tensor |
TransformerImpl.forward(Tensor src,
Tensor tgt,
Tensor src_mask,
Tensor tgt_mask,
Tensor memory_mask,
Tensor src_key_padding_mask,
Tensor tgt_key_padding_mask,
Tensor memory_key_padding_mask)
forward function for Transformer Module
Args:
src: the sequence to the encoder (required).
|
Tensor |
Tensor.frac_() |
Tensor |
Tensor.frac() |
Tensor |
TensorArrayRef.front()
front - Get the first element.
|
Tensor |
AutogradMetaInterface.fw_grad(long level,
TensorBase self) |
Tensor |
AutogradMeta.fw_grad(long level,
TensorBase self) |
Tensor |
Tensor.gather(Dimname dim,
Tensor index) |
Tensor |
Tensor.gather(Dimname dim,
Tensor index,
boolean sparse_grad) |
Tensor |
Tensor.gather(long dim,
Tensor index) |
Tensor |
Tensor.gather(long dim,
Tensor index,
boolean sparse_grad) |
Tensor |
Tensor.gcd_(Tensor other) |
Tensor |
Tensor.gcd(Tensor other) |
Tensor |
Tensor.ge_(Scalar other) |
Tensor |
Tensor.ge_(Tensor other) |
Tensor |
Tensor.ge(Scalar other) |
Tensor |
Tensor.ge(Tensor other) |
static Tensor |
TransformerImpl.generate_square_subsequent_mask(long sz)
Generate a square mask for the sequence.
|
Tensor |
Tensor.geometric_(double p) |
Tensor |
Tensor.geometric_(double p,
GeneratorOptional generator) |
Tensor |
Tensor.ger(Tensor vec2) |
Tensor |
Generator.get_state() |
Tensor[] |
TensorVector.get() |
Tensor |
TensorVector.Iterator.get() |
Tensor |
TensorOptional.get() |
Tensor[] |
TensorDeque.get() |
Tensor |
TensorDeque.Iterator.get() |
Tensor |
StringTensorMap.get(BytePointer i) |
Tensor |
StringTensorDict.get(BytePointer i) |
Tensor |
ParameterDictImpl.get(BytePointer key)
Returns the value associated with the given
key. |
Tensor |
TensorVector.get(long i) |
Tensor |
TensorDeque.get(long i) |
Tensor |
TensorArrayRef.get(long Index)
\}
\name Operator Overloads
\{
|
Tensor |
Tensor.get(long index) |
Tensor |
ParameterListImpl.get(long idx)
Returns the value associated with the given
key. |
Tensor |
Tensor.get(Scalar index) |
Tensor |
ParameterDictImpl.get(String key) |
Tensor |
Tensor.get(Tensor index) |
Tensor |
TensorTuple.get0() |
Tensor |
TensorTensorTuple.get0() |
Tensor |
TensorTensorTensorTupleTuple.get0() |
Tensor |
TensorTensorTensorTuple.get0() |
Tensor |
TensorTensorTensorTensorVectorTuple.get0() |
Tensor |
TensorTensorTensorTensorTuple.get0() |
Tensor |
TensorTensorTensorTensorTensorTuple.get0() |
Tensor |
TensorTensorTensorTensorLongTuple.get0() |
Tensor |
TensorTensorDoubleLongTuple.get0() |
static Tensor |
TensorTensorDoubleLongTuple.get0(TensorTensorDoubleLongTuple container) |
static Tensor |
TensorTensorTensorTensorLongTuple.get0(TensorTensorTensorTensorLongTuple container) |
static Tensor |
TensorTensorTensorTensorTensorTuple.get0(TensorTensorTensorTensorTensorTuple container) |
static Tensor |
TensorTensorTensorTensorTuple.get0(TensorTensorTensorTensorTuple container) |
static Tensor |
TensorTensorTensorTensorVectorTuple.get0(TensorTensorTensorTensorVectorTuple container) |
static Tensor |
TensorTensorTensorTuple.get0(TensorTensorTensorTuple container) |
static Tensor |
TensorTensorTensorTupleTuple.get0(TensorTensorTensorTupleTuple container) |
static Tensor |
TensorTensorTuple.get0(TensorTensorTuple container) |
static Tensor |
TensorTuple.get0(TensorTuple container) |
Tensor |
TensorTensorTuple.get1() |
Tensor |
TensorTensorTensorTuple.get1() |
Tensor |
TensorTensorTensorTensorVectorTuple.get1() |
Tensor |
TensorTensorTensorTensorTuple.get1() |
Tensor |
TensorTensorTensorTensorTensorTuple.get1() |
Tensor |
TensorTensorTensorTensorLongTuple.get1() |
Tensor |
TensorTensorDoubleLongTuple.get1() |
Tensor |
PackedSequenceTensorTuple.get1() |
static Tensor |
PackedSequenceTensorTuple.get1(PackedSequenceTensorTuple container) |
static Tensor |
TensorTensorDoubleLongTuple.get1(TensorTensorDoubleLongTuple container) |
static Tensor |
TensorTensorTensorTensorLongTuple.get1(TensorTensorTensorTensorLongTuple container) |
static Tensor |
TensorTensorTensorTensorTensorTuple.get1(TensorTensorTensorTensorTensorTuple container) |
static Tensor |
TensorTensorTensorTensorTuple.get1(TensorTensorTensorTensorTuple container) |
static Tensor |
TensorTensorTensorTensorVectorTuple.get1(TensorTensorTensorTensorVectorTuple container) |
static Tensor |
TensorTensorTensorTuple.get1(TensorTensorTensorTuple container) |
static Tensor |
TensorTensorTuple.get1(TensorTensorTuple container) |
Tensor |
TensorTensorTensorTuple.get2() |
Tensor |
TensorTensorTensorTensorVectorTuple.get2() |
Tensor |
TensorTensorTensorTensorTuple.get2() |
Tensor |
TensorTensorTensorTensorTensorTuple.get2() |
Tensor |
TensorTensorTensorTensorLongTuple.get2() |
static Tensor |
TensorTensorTensorTensorLongTuple.get2(TensorTensorTensorTensorLongTuple container) |
static Tensor |
TensorTensorTensorTensorTensorTuple.get2(TensorTensorTensorTensorTensorTuple container) |
static Tensor |
TensorTensorTensorTensorTuple.get2(TensorTensorTensorTensorTuple container) |
static Tensor |
TensorTensorTensorTensorVectorTuple.get2(TensorTensorTensorTensorVectorTuple container) |
static Tensor |
TensorTensorTensorTuple.get2(TensorTensorTensorTuple container) |
Tensor |
TensorTensorTensorTensorTuple.get3() |
Tensor |
TensorTensorTensorTensorTensorTuple.get3() |
Tensor |
TensorTensorTensorTensorLongTuple.get3() |
static Tensor |
TensorTensorTensorTensorLongTuple.get3(TensorTensorTensorTensorLongTuple container) |
static Tensor |
TensorTensorTensorTensorTensorTuple.get3(TensorTensorTensorTensorTensorTuple container) |
static Tensor |
TensorTensorTensorTensorTuple.get3(TensorTensorTensorTensorTuple container) |
Tensor |
TensorTensorTensorTensorTensorTuple.get4() |
static Tensor |
TensorTensorTensorTensorTensorTuple.get4(TensorTensorTensorTensorTensorTuple container) |
Tensor |
Tensor.getPointer(long i) |
Tensor |
OptionalTensorRef.getTensorRef() |
Tensor |
AutogradMeta.grad_() |
Tensor |
RMSpropParamState.grad_avg() |
Tensor |
TensorImpl.grad()
Return the accumulated gradient of a tensor.
|
Tensor |
Tensor.grad()
This function returns an undefined tensor by default and returns a defined tensor
the first time a call to
backward() computes gradients for this Tensor. |
Tensor |
AutogradMetaInterface.grad() |
Tensor |
AutogradMeta.grad() |
Tensor |
Tensor.greater_(Scalar other) |
Tensor |
Tensor.greater_(Tensor other) |
Tensor |
Tensor.greater_equal_(Scalar other) |
Tensor |
Tensor.greater_equal_(Tensor other) |
Tensor |
Tensor.greater_equal(Scalar other) |
Tensor |
Tensor.greater_equal(Tensor other) |
Tensor |
Tensor.greater(Scalar other) |
Tensor |
Tensor.greater(Tensor other) |
Tensor |
Tensor.gt_(Scalar other) |
Tensor |
Tensor.gt_(Tensor other) |
Tensor |
Tensor.gt(Scalar other) |
Tensor |
Tensor.gt(Tensor other) |
Tensor |
LBFGSParamState.H_diag() |
Tensor |
Tensor.hardshrink_backward(Tensor grad_out,
Scalar lambd) |
Tensor |
Tensor.hardshrink() |
Tensor |
Tensor.hardshrink(Scalar lambd) |
Tensor |
Tensor.heaviside_(Tensor values) |
Tensor |
Tensor.heaviside(Tensor values) |
Tensor |
Tensor.hip() |
Tensor |
Tensor.histc() |
Tensor |
Tensor.histc(long bins,
Scalar min,
Scalar max) |
Tensor |
Tensor.hypot_(Tensor other) |
Tensor |
Tensor.hypot(Tensor other) |
Tensor |
Tensor.i0_() |
Tensor |
Tensor.i0() |
Tensor |
Tensor.igamma_(Tensor other) |
Tensor |
Tensor.igamma(Tensor other) |
Tensor |
Tensor.igammac_(Tensor other) |
Tensor |
Tensor.igammac(Tensor other) |
Tensor |
MNIST.images()
Returns all images stacked into a single tensor.
|
Tensor |
MultiheadAttentionImpl.in_proj_bias() |
Tensor |
MultiheadAttentionForwardFuncOptions.in_proj_bias() |
Tensor |
MultiheadAttentionImpl.in_proj_weight() |
Tensor |
MultiheadAttentionForwardFuncOptions.in_proj_weight() |
Tensor |
Tensor.index_add_(long dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_add_(long dim,
Tensor index,
Tensor source,
Scalar alpha) |
Tensor |
Tensor.index_add(Dimname dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_add(Dimname dim,
Tensor index,
Tensor source,
Scalar alpha) |
Tensor |
Tensor.index_add(long dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_add(long dim,
Tensor index,
Tensor source,
Scalar alpha) |
Tensor |
Tensor.index_copy_(Dimname dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_copy_(long dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_copy(Dimname dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_copy(long dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_fill_(Dimname dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.index_fill_(Dimname dim,
Tensor index,
Tensor value) |
Tensor |
Tensor.index_fill_(long dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.index_fill_(long dim,
Tensor index,
Tensor value) |
Tensor |
Tensor.index_fill(Dimname dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.index_fill(Dimname dim,
Tensor index,
Tensor value) |
Tensor |
Tensor.index_fill(long dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.index_fill(long dim,
Tensor index,
Tensor value) |
Tensor |
Tensor.index_put_(TensorIndexArrayRef indices,
Scalar v) |
Tensor |
Tensor.index_put_(TensorIndexArrayRef indices,
Tensor rhs) |
Tensor |
Tensor.index_select(Dimname dim,
Tensor index) |
Tensor |
Tensor.index_select(long dim,
Tensor index) |
Tensor |
Tensor.index(TensorIndexArrayRef indices) |
Tensor |
Tensor.indices() |
Tensor |
Tensor.inner(Tensor other) |
Tensor |
ParameterDictImpl.insert(BytePointer key,
Tensor param)
Insert the parameter along with the key into ParameterDict
The parameter is set to be require grad by default
|
Tensor |
ParameterDictImpl.insert(String key,
Tensor param) |
Tensor |
Tensor.int_repr() |
Tensor |
Tensor.inverse() |
Tensor |
Tensor.isclose(Tensor other) |
Tensor |
Tensor.isclose(Tensor other,
double rtol,
double atol,
boolean equal_nan) |
Tensor |
Tensor.isfinite() |
Tensor |
Tensor.isinf() |
Tensor |
Tensor.isnan() |
Tensor |
Tensor.isneginf() |
Tensor |
Tensor.isposinf() |
Tensor |
Tensor.isreal() |
Tensor |
Tensor.istft(long n_fft) |
Tensor |
Tensor.istft(long n_fft,
LongOptional hop_length,
LongOptional win_length,
TensorOptional window,
boolean center,
boolean normalized,
BoolOptional onesided,
LongOptional length,
boolean return_complex) |
Tensor |
MultiheadAttentionImpl.k_proj_weight() |
Tensor |
MultiheadAttentionForwardFuncOptions.k_proj_weight() |
Tensor |
MultiheadAttentionForwardFuncOptions.key_padding_mask() |
Tensor |
Tensor.kron(Tensor other) |
Tensor |
Tensor.lcm_(Tensor other) |
Tensor |
Tensor.lcm(Tensor other) |
Tensor |
Tensor.ldexp_(Tensor other) |
Tensor |
Tensor.ldexp(Tensor other) |
Tensor |
Tensor.le_(Scalar other) |
Tensor |
Tensor.le_(Tensor other) |
Tensor |
Tensor.le(Scalar other) |
Tensor |
Tensor.le(Tensor other) |
Tensor |
Tensor.lerp_(Tensor end,
Scalar weight) |
Tensor |
Tensor.lerp_(Tensor end,
Tensor weight) |
Tensor |
Tensor.lerp(Tensor end,
Scalar weight) |
Tensor |
Tensor.lerp(Tensor end,
Tensor weight) |
Tensor |
Tensor.less_(Scalar other) |
Tensor |
Tensor.less_(Tensor other) |
Tensor |
Tensor.less_equal_(Scalar other) |
Tensor |
Tensor.less_equal_(Tensor other) |
Tensor |
Tensor.less_equal(Scalar other) |
Tensor |
Tensor.less_equal(Tensor other) |
Tensor |
Tensor.less(Scalar other) |
Tensor |
Tensor.less(Tensor other) |
Tensor |
Tensor.lgamma_() |
Tensor |
Tensor.lgamma() |
Tensor |
Tensor.log_() |
Tensor |
Tensor.log_normal_() |
Tensor |
Tensor.log_normal_(double mean,
double std,
GeneratorOptional generator) |
Tensor |
AdaptiveLogSoftmaxWithLossImpl.log_prob(Tensor input)
Computes log probabilities for all n_classes
|
Tensor |
Tensor.log_softmax(Dimname dim) |
Tensor |
Tensor.log_softmax(Dimname dim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.log_softmax(long dim) |
Tensor |
Tensor.log_softmax(long dim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.log() |
Tensor |
Tensor.log10_() |
Tensor |
Tensor.log10() |
Tensor |
Tensor.log1p_() |
Tensor |
Tensor.log1p() |
Tensor |
Tensor.log2_() |
Tensor |
Tensor.log2() |
Tensor |
Tensor.logaddexp(Tensor other) |
Tensor |
Tensor.logaddexp2(Tensor other) |
Tensor |
Tensor.logcumsumexp(Dimname dim) |
Tensor |
Tensor.logcumsumexp(long dim) |
Tensor |
Tensor.logdet() |
Tensor |
Tensor.logical_and_(Tensor other) |
Tensor |
Tensor.logical_and(Tensor other) |
Tensor |
Tensor.logical_not_() |
Tensor |
Tensor.logical_not() |
Tensor |
Tensor.logical_or_(Tensor other) |
Tensor |
Tensor.logical_or(Tensor other) |
Tensor |
Tensor.logical_xor_(Tensor other) |
Tensor |
Tensor.logical_xor(Tensor other) |
Tensor |
Tensor.logit_() |
Tensor |
Tensor.logit_(DoubleOptional eps) |
Tensor |
Tensor.logit() |
Tensor |
Tensor.logit(DoubleOptional eps) |
Tensor |
Tensor.logsumexp(DimnameArrayRef dim) |
Tensor |
Tensor.logsumexp(DimnameArrayRef dim,
boolean keepdim) |
Tensor |
Tensor.logsumexp(long... dim) |
Tensor |
Tensor.logsumexp(long[] dim,
boolean keepdim) |
Tensor |
Tensor.logsumexp(LongArrayRef dim) |
Tensor |
Tensor.logsumexp(LongArrayRef dim,
boolean keepdim) |
Tensor |
Tensor.lt_(Scalar other) |
Tensor |
Tensor.lt_(Tensor other) |
Tensor |
Tensor.lt(Scalar other) |
Tensor |
Tensor.lt(Tensor other) |
Tensor |
Tensor.lu_solve(Tensor LU_data,
Tensor LU_pivots) |
Tensor |
TensorMaker.make_tensor() |
Tensor |
Tensor.masked_fill_(Tensor mask,
Scalar value) |
Tensor |
Tensor.masked_fill_(Tensor mask,
Tensor value) |
Tensor |
Tensor.masked_fill(Tensor mask,
Scalar value) |
Tensor |
Tensor.masked_fill(Tensor mask,
Tensor value) |
Tensor |
Tensor.masked_scatter_(Tensor mask,
Tensor source) |
Tensor |
Tensor.masked_scatter(Tensor mask,
Tensor source) |
Tensor |
Tensor.masked_select(Tensor mask) |
Tensor |
Tensor.matmul(Tensor other) |
Tensor |
Tensor.matrix_exp() |
Tensor |
Tensor.matrix_power(long n) |
Tensor |
AdamWParamState.max_exp_avg_sq() |
Tensor |
AdamParamState.max_exp_avg_sq() |
Tensor |
Tensor.max() |
Tensor |
Tensor.max(Tensor other) |
Tensor |
Tensor.maximum(Tensor other) |
Tensor |
Tensor.mean() |
Tensor |
Tensor.mean(DimnameArrayRef dim) |
Tensor |
Tensor.mean(DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.mean(long... dim) |
Tensor |
Tensor.mean(long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.mean(LongArrayRef dim) |
Tensor |
Tensor.mean(LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.mean(ScalarTypeOptional dtype) |
Tensor |
Tensor.median() |
Tensor |
Tensor.metal() |
Tensor |
Tensor.min() |
Tensor |
Tensor.min(Tensor other) |
Tensor |
Tensor.minimum(Tensor other) |
Tensor |
Tensor.mm(Tensor mat2) |
Tensor |
SGDParamState.momentum_buffer() |
Tensor |
RMSpropParamState.momentum_buffer() |
Tensor |
Tensor.moveaxis(long[] source,
long... destination) |
Tensor |
Tensor.moveaxis(LongArrayRef source,
LongArrayRef destination) |
Tensor |
Tensor.moveaxis(long source,
long destination) |
Tensor |
Tensor.movedim(long[] source,
long... destination) |
Tensor |
Tensor.movedim(LongArrayRef source,
LongArrayRef destination) |
Tensor |
Tensor.movedim(long source,
long destination) |
Tensor |
Tensor.msort() |
Tensor |
Tensor.mul_(Scalar other) |
Tensor |
Tensor.mul_(Tensor other) |
Tensor |
Tensor.mul(Scalar other) |
Tensor |
Tensor.mul(Tensor other) |
Tensor |
Tensor.multinomial(long num_samples) |
Tensor |
Tensor.multinomial(long num_samples,
boolean replacement,
GeneratorOptional generator) |
Tensor |
Tensor.multiply_(Scalar other) |
Tensor |
Tensor.multiply_(Tensor other) |
Tensor |
parameter_iterator.multiply() |
Tensor |
buffer_iterator.multiply() |
Tensor |
TensorMaybeOwned.multiply() |
Tensor |
TensorArg.multiply() |
Tensor |
OptionalTensorRef.multiply() |
Tensor |
Tensor.multiply(Scalar other) |
Tensor |
Tensor.multiply(Tensor other) |
Tensor |
Tensor.multiplyPut(Scalar other) |
Tensor |
Tensor.multiplyPut(Tensor other) |
Tensor |
TensorImpl.mutable_grad()
Return a mutable reference to the gradient.
|
Tensor |
Tensor.mutable_grad()
Return a mutable reference to the gradient.
|
Tensor |
AutogradMetaInterface.mutable_grad() |
Tensor |
AutogradMeta.mutable_grad()
Accesses the gradient
Variable of this Variable. |
Tensor |
Tensor.mv(Tensor vec) |
Tensor |
Tensor.mvlgamma_(long p) |
Tensor |
Tensor.mvlgamma(long p) |
Tensor |
Tensor.nan_to_num_() |
Tensor |
Tensor.nan_to_num_(DoubleOptional nan,
DoubleOptional posinf,
DoubleOptional neginf) |
Tensor |
Tensor.nan_to_num() |
Tensor |
Tensor.nan_to_num(DoubleOptional nan,
DoubleOptional posinf,
DoubleOptional neginf) |
Tensor |
Tensor.nanmean() |
Tensor |
Tensor.nanmean(long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.nanmean(LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.nanmedian() |
Tensor |
Tensor.nanquantile(double q) |
Tensor |
Tensor.nanquantile(double q,
LongOptional dim,
boolean keepdim) |
Tensor |
Tensor.nanquantile(double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
Tensor |
Tensor.nanquantile(Tensor q) |
Tensor |
Tensor.nanquantile(Tensor q,
LongOptional dim,
boolean keepdim) |
Tensor |
Tensor.nanquantile(Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
Tensor |
Tensor.nansum() |
Tensor |
Tensor.nansum(long... dim) |
Tensor |
Tensor.nansum(long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.nansum(LongArrayRef dim) |
Tensor |
Tensor.nansum(LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.nansum(ScalarTypeOptional dtype) |
Tensor |
Tensor.narrow_copy(long dim,
long start,
long length) |
Tensor |
Tensor.narrow(long dim,
long start,
long length) |
Tensor |
Tensor.narrow(long dim,
Tensor start,
long length) |
Tensor |
Tensor.ne_(Scalar other) |
Tensor |
Tensor.ne_(Tensor other) |
Tensor |
Tensor.ne(Scalar other) |
Tensor |
Tensor.ne(Tensor other) |
Tensor |
Tensor.neg_() |
Tensor |
Tensor.neg() |
Tensor |
Tensor.negative_() |
Tensor |
Tensor.negative() |
Tensor |
Tensor.new_empty_strided(long[] size,
long... stride) |
Tensor |
Tensor.new_empty_strided(long[] size,
long[] stride,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
Tensor |
Tensor.new_empty_strided(long[] size,
long[] stride,
TensorOptions options) |
Tensor |
Tensor.new_empty_strided(LongArrayRef size,
LongArrayRef stride) |
Tensor |
Tensor.new_empty_strided(LongArrayRef size,
LongArrayRef stride,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
Tensor |
Tensor.new_empty_strided(LongArrayRef size,
LongArrayRef stride,
TensorOptions options) |
Tensor |
Tensor.new_empty(long... size) |
Tensor |
Tensor.new_empty(long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
Tensor |
Tensor.new_empty(long[] size,
TensorOptions options) |
Tensor |
Tensor.new_empty(LongArrayRef size) |
Tensor |
Tensor.new_empty(LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
Tensor |
Tensor.new_empty(LongArrayRef size,
TensorOptions options) |
Tensor |
Tensor.new_full(long[] size,
Scalar fill_value) |
Tensor |
Tensor.new_full(long[] size,
Scalar fill_value,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
Tensor |
Tensor.new_full(long[] size,
Scalar fill_value,
TensorOptions options) |
Tensor |
Tensor.new_full(LongArrayRef size,
Scalar fill_value) |
Tensor |
Tensor.new_full(LongArrayRef size,
Scalar fill_value,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
Tensor |
Tensor.new_full(LongArrayRef size,
Scalar fill_value,
TensorOptions options) |
Tensor |
Tensor.new_ones(long... size) |
Tensor |
Tensor.new_ones(long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
Tensor |
Tensor.new_ones(long[] size,
TensorOptions options) |
Tensor |
Tensor.new_ones(LongArrayRef size) |
Tensor |
Tensor.new_ones(LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
Tensor |
Tensor.new_ones(LongArrayRef size,
TensorOptions options) |
Tensor |
Tensor.new_zeros(long... size) |
Tensor |
Tensor.new_zeros(long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
Tensor |
Tensor.new_zeros(long[] size,
TensorOptions options) |
Tensor |
Tensor.new_zeros(LongArrayRef size) |
Tensor |
Tensor.new_zeros(LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
Tensor |
Tensor.new_zeros(LongArrayRef size,
TensorOptions options) |
Tensor |
Tensor.nextafter_(Tensor other) |
Tensor |
Tensor.nextafter(Tensor other) |
Tensor |
Tensor.nonzero() |
Tensor |
Tensor.norm() |
Tensor |
Tensor.norm(Scalar p) |
Tensor |
Tensor.norm(ScalarOptional p,
DimnameArrayRef dim) |
Tensor |
Tensor.norm(ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim) |
Tensor |
Tensor.norm(ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
Tensor |
Tensor.norm(ScalarOptional p,
long... dim) |
Tensor |
Tensor.norm(ScalarOptional p,
long[] dim,
boolean keepdim) |
Tensor |
Tensor.norm(ScalarOptional p,
long[] dim,
boolean keepdim,
torch.ScalarType dtype) |
Tensor |
Tensor.norm(ScalarOptional p,
LongArrayRef dim) |
Tensor |
Tensor.norm(ScalarOptional p,
LongArrayRef dim,
boolean keepdim) |
Tensor |
Tensor.norm(ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
Tensor |
Tensor.norm(ScalarOptional p,
torch.ScalarType dtype) |
Tensor |
Tensor.normal_() |
Tensor |
Tensor.normal_(double mean,
double std,
GeneratorOptional generator) |
Tensor |
Tensor.not_equal_(Scalar other) |
Tensor |
Tensor.not_equal_(Tensor other) |
Tensor |
Tensor.not_equal(Scalar other) |
Tensor |
Tensor.not_equal(Tensor other) |
Tensor |
Tensor.not() |
Tensor |
InstanceNorm3dImplBaseBase.num_batches_tracked()
The number of the forward call.
|
Tensor |
InstanceNorm2dImplBaseBase.num_batches_tracked()
The number of the forward call.
|
Tensor |
InstanceNorm1dImplBaseBase.num_batches_tracked()
The number of the forward call.
|
Tensor |
BatchNorm3dImplBaseBase.num_batches_tracked()
The number of the forward call.
|
Tensor |
BatchNorm2dImplBaseBase.num_batches_tracked()
The number of the forward call.
|
Tensor |
BatchNorm1dImplBaseBase.num_batches_tracked()
The number of the forward call.
|
Tensor |
Tensor.numpy_T() |
Tensor |
EmbeddingBagFuncOptions.offsets() |
Tensor |
Tensor.orgqr(Tensor input2) |
Tensor |
Tensor.ormqr(Tensor input2,
Tensor input3) |
Tensor |
Tensor.ormqr(Tensor input2,
Tensor input3,
boolean left,
boolean transpose) |
Tensor |
Tensor.orPut(Tensor other) |
Tensor |
MultiheadAttentionForwardFuncOptions.out_proj_bias() |
Tensor |
MultiheadAttentionForwardFuncOptions.out_proj_weight() |
Tensor |
Tensor.outer(Tensor vec2) |
Tensor |
ASMoutput.output()
Tensor containing computed target log probabilities for each example
|
Tensor |
EmbeddingBagFuncOptions.per_sample_weights() |
Tensor |
Tensor.permute(long... dims) |
Tensor |
Tensor.permute(LongArrayRef dims) |
Tensor |
Tensor.pin_memory() |
Tensor |
Tensor.pin_memory(DeviceOptional device) |
Tensor |
Tensor.pinverse() |
Tensor |
Tensor.pinverse(double rcond) |
Tensor |
Tensor.polygamma_(long n) |
Tensor |
Tensor.polygamma(long n) |
Tensor |
TensorVector.pop_back() |
Tensor |
TensorDeque.pop_back() |
Tensor |
ParameterDictImpl.pop(BytePointer key)
Remove key from the ParameterDict and return its value, throw exception
if the key is not contained.
|
Tensor |
ParameterDictImpl.pop(String key) |
Tensor |
BCEWithLogitsLossOptions.pos_weight() |
Tensor |
BCEWithLogitsLossImpl.pos_weight()
A weight of positive examples.
|
Tensor |
Tensor.position(long position) |
Tensor |
Tensor.positive() |
Tensor |
Tensor.pow_(Scalar exponent) |
Tensor |
Tensor.pow_(Tensor exponent) |
Tensor |
Tensor.pow(Scalar exponent) |
Tensor |
Tensor.pow(Tensor exponent) |
Tensor |
AdaptiveLogSoftmaxWithLossImpl.predict(Tensor input)
This is equivalent to
log_pob(input).argmax(1) but is more efficient in some cases |
Tensor |
Tensor.prelu(Tensor weight) |
Tensor |
LBFGSParamState.prev_flat_grad() |
Tensor |
Tensor.prod() |
Tensor |
Tensor.prod(Dimname dim) |
Tensor |
Tensor.prod(Dimname dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.prod(long dim) |
Tensor |
Tensor.prod(long dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.prod(ScalarTypeOptional dtype) |
Tensor |
Tensor.put_(Tensor index,
Tensor source) |
Tensor |
Tensor.put_(Tensor index,
Tensor source,
boolean accumulate) |
Tensor |
Tensor.put(Tensor x) |
Tensor |
Tensor.put(TensorBase x) |
Tensor |
Tensor.put(Tensor index,
Tensor source) |
Tensor |
Tensor.put(Tensor index,
Tensor source,
boolean accumulate) |
Tensor |
Tensor.q_per_channel_scales() |
Tensor |
Tensor.q_per_channel_zero_points() |
Tensor |
MultiheadAttentionImpl.q_proj_weight() |
Tensor |
MultiheadAttentionForwardFuncOptions.q_proj_weight() |
Tensor |
Tensor.quantile(double q) |
Tensor |
Tensor.quantile(double q,
LongOptional dim,
boolean keepdim) |
Tensor |
Tensor.quantile(double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
Tensor |
Tensor.quantile(Tensor q) |
Tensor |
Tensor.quantile(Tensor q,
LongOptional dim,
boolean keepdim) |
Tensor |
Tensor.quantile(Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
Tensor |
Tensor.rad2deg_() |
Tensor |
Tensor.rad2deg() |
Tensor |
Tensor.random_() |
Tensor |
Tensor.random_(GeneratorOptional generator) |
Tensor |
Tensor.random_(long to) |
Tensor |
Tensor.random_(long to,
GeneratorOptional generator) |
Tensor |
Tensor.random_(long from,
LongOptional to) |
Tensor |
Tensor.random_(long from,
LongOptional to,
GeneratorOptional generator) |
Tensor |
Tensor.ravel() |
Tensor |
Tensor.reciprocal_() |
Tensor |
Tensor.reciprocal() |
Tensor |
Tensor.refine_names(DimnameArrayRef names) |
Tensor |
Module.register_buffer(BytePointer name,
Tensor tensor)
Registers a buffer with this
Module. |
Tensor |
Module.register_buffer(String name,
Tensor tensor) |
Tensor |
Module.register_parameter(BytePointer name,
Tensor tensor) |
Tensor |
Module.register_parameter(BytePointer name,
Tensor tensor,
boolean requires_grad)
Registers a parameter with this
Module. |
Tensor |
Module.register_parameter(String name,
Tensor tensor) |
Tensor |
Module.register_parameter(String name,
Tensor tensor,
boolean requires_grad) |
Tensor |
Tensor.relu_() |
Tensor |
Tensor.relu() |
Tensor |
Tensor.remainder_(Scalar other) |
Tensor |
Tensor.remainder_(Tensor other) |
Tensor |
Tensor.remainder(Scalar other) |
Tensor |
Tensor.remainder(Tensor other) |
Tensor |
Tensor.rename_(DimnameListOptional names) |
Tensor |
Tensor.rename(DimnameListOptional names) |
Tensor |
Tensor.renorm_(Scalar p,
long dim,
Scalar maxnorm) |
Tensor |
Tensor.renorm(Scalar p,
long dim,
Scalar maxnorm) |
Tensor |
Tensor.repeat_interleave(long repeats) |
Tensor |
Tensor.repeat_interleave(long repeats,
LongOptional dim,
LongOptional output_size) |
Tensor |
Tensor.repeat_interleave(Tensor repeats) |
Tensor |
Tensor.repeat_interleave(Tensor repeats,
LongOptional dim,
LongOptional output_size) |
Tensor |
Tensor.repeat(long... repeats) |
Tensor |
Tensor.repeat(LongArrayRef repeats) |
Tensor |
Tensor.requires_grad_() |
Tensor |
Tensor.requires_grad_(boolean _requires_grad) |
Tensor |
Tensor.reshape_as(Tensor other) |
Tensor |
Tensor.reshape(long... shape) |
Tensor |
Tensor.reshape(LongArrayRef shape) |
Tensor |
Tensor.resize_(long... size) |
Tensor |
Tensor.resize_(long[] size,
MemoryFormatOptional memory_format) |
Tensor |
Tensor.resize_(LongArrayRef size) |
Tensor |
Tensor.resize_(LongArrayRef size,
MemoryFormatOptional memory_format) |
Tensor |
Tensor.resize_as_(Tensor the_template) |
Tensor |
Tensor.resize_as_(Tensor the_template,
MemoryFormatOptional memory_format) |
Tensor |
Tensor.resolve_conj() |
Tensor |
Tensor.resolve_neg() |
Tensor |
Tensor.roll(long... shifts) |
Tensor |
Tensor.roll(long[] shifts,
long... dims) |
Tensor |
Tensor.roll(LongArrayRef shifts) |
Tensor |
Tensor.roll(LongArrayRef shifts,
LongArrayRef dims) |
Tensor |
Tensor.rot90() |
Tensor |
Tensor.rot90(long k,
long... dims) |
Tensor |
Tensor.rot90(long k,
LongArrayRef dims) |
Tensor |
Tensor.round_() |
Tensor |
Tensor.round() |
Tensor |
Tensor.rsqrt_() |
Tensor |
Tensor.rsqrt() |
Tensor |
InstanceNormFuncOptions.running_mean() |
Tensor |
InstanceNorm3dImplBaseBase.running_mean()
The running mean.
|
Tensor |
InstanceNorm2dImplBaseBase.running_mean()
The running mean.
|
Tensor |
InstanceNorm1dImplBaseBase.running_mean()
The running mean.
|
Tensor |
BatchNorm3dImplBaseBase.running_mean()
The running mean.
|
Tensor |
BatchNorm2dImplBaseBase.running_mean()
The running mean.
|
Tensor |
BatchNorm1dImplBaseBase.running_mean()
The running mean.
|
Tensor |
InstanceNormFuncOptions.running_var() |
Tensor |
InstanceNorm3dImplBaseBase.running_var()
The running variance.
|
Tensor |
InstanceNorm2dImplBaseBase.running_var()
The running variance.
|
Tensor |
InstanceNorm1dImplBaseBase.running_var()
The running variance.
|
Tensor |
BatchNorm3dImplBaseBase.running_var()
The running variance.
|
Tensor |
BatchNorm2dImplBaseBase.running_var()
The running variance.
|
Tensor |
BatchNorm1dImplBaseBase.running_var()
The running variance.
|
Tensor |
Tensor.scatter_(long dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.scatter_(long dim,
Tensor index,
Scalar value,
Pointer reduce) |
Tensor |
Tensor.scatter_(long dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter_(long dim,
Tensor index,
Tensor src,
Pointer reduce) |
Tensor |
Tensor.scatter_add_(long dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter_add(Dimname dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter_add(long dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter(Dimname dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.scatter(Dimname dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter(long dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.scatter(long dim,
Tensor index,
Scalar value,
Pointer reduce) |
Tensor |
Tensor.scatter(long dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter(long dim,
Tensor index,
Tensor src,
Pointer reduce) |
Tensor |
StringTensorPair.second() |
Tensor |
StringTensorMap.Iterator.second() |
Tensor |
StringTensorDict.Iterator.second() |
Tensor |
Tensor.select(Dimname dim,
long index) |
Tensor |
Tensor.select(long dim,
long index) |
Tensor |
Tensor.set_() |
Tensor |
Tensor.set_(Storage source) |
Tensor |
Tensor.set_(Storage source,
long storage_offset,
long... size) |
Tensor |
Tensor.set_(Storage source,
long storage_offset,
long[] size,
long... stride) |
Tensor |
Tensor.set_(Storage source,
long storage_offset,
LongArrayRef size) |
Tensor |
Tensor.set_(Storage source,
long storage_offset,
LongArrayRef size,
LongArrayRef stride) |
Tensor |
Tensor.set_(Tensor source) |
Tensor |
Tensor.set_requires_grad(boolean requires_grad)
\fn Tensor detach() const;
Returns a new Tensor, detached from the current graph.
|
Tensor |
Tensor.sgn_() |
Tensor |
Tensor.sgn() |
Tensor |
Tensor.sigmoid_() |
Tensor |
Tensor.sigmoid() |
Tensor |
Tensor.sign_() |
Tensor |
Tensor.sign() |
Tensor |
Tensor.signbit() |
Tensor |
Tensor.sin_() |
Tensor |
Tensor.sin() |
Tensor |
Tensor.sinc_() |
Tensor |
Tensor.sinc() |
Tensor |
Tensor.sinh_() |
Tensor |
Tensor.sinh() |
Tensor |
Tensor.slice() |
Tensor |
Tensor.slice(long dim,
LongOptional start,
LongOptional end,
long step) |
Tensor |
Tensor.smm(Tensor mat2) |
Tensor |
Tensor.softmax(Dimname dim) |
Tensor |
Tensor.softmax(Dimname dim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.softmax(long dim) |
Tensor |
Tensor.softmax(long dim,
ScalarTypeOptional dtype) |
Tensor |
PackedSequence.sorted_indices() |
Tensor |
Tensor.sparse_mask(Tensor mask) |
Tensor |
Tensor.sparse_resize_(long[] size,
long sparse_dim,
long dense_dim) |
Tensor |
Tensor.sparse_resize_(LongArrayRef size,
long sparse_dim,
long dense_dim) |
Tensor |
Tensor.sparse_resize_and_clear_(long[] size,
long sparse_dim,
long dense_dim) |
Tensor |
Tensor.sparse_resize_and_clear_(LongArrayRef size,
long sparse_dim,
long dense_dim) |
Tensor |
Tensor.special_polygamma(long n) |
Tensor |
Tensor.sqrt_() |
Tensor |
Tensor.sqrt() |
Tensor |
Tensor.square_() |
Tensor |
RMSpropParamState.square_avg() |
Tensor |
Tensor.square() |
Tensor |
Tensor.squeeze_() |
Tensor |
Tensor.squeeze_(Dimname dim) |
Tensor |
Tensor.squeeze_(long dim) |
Tensor |
Tensor.squeeze() |
Tensor |
Tensor.squeeze(Dimname dim) |
Tensor |
Tensor.squeeze(long dim) |
Tensor |
Tensor.sspaddmm(Tensor mat1,
Tensor mat2) |
Tensor |
Tensor.sspaddmm(Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
Tensor |
MultiheadAttentionForwardFuncOptions.static_k() |
Tensor |
MultiheadAttentionForwardFuncOptions.static_v() |
Tensor |
Tensor.std() |
Tensor |
Tensor.std(boolean unbiased) |
Tensor |
Tensor.std(DimnameArrayRef dim) |
Tensor |
Tensor.std(DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
Tensor |
Tensor.std(DimnameArrayRef dim,
LongOptional correction) |
Tensor |
Tensor.std(DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
Tensor |
Tensor.std(int dim) |
Tensor |
Tensor.std(long... dim) |
Tensor |
Tensor.std(long[] dim,
boolean unbiased,
boolean keepdim) |
Tensor |
Tensor.std(LongArrayRef dim) |
Tensor |
Tensor.std(LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
Tensor |
Tensor.std(LongArrayRefOptional dim,
LongOptional correction) |
Tensor |
Tensor.std(LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
Tensor |
SGD.step() |
Tensor |
RMSprop.step() |
Tensor |
Optimizer.step() |
Tensor |
AdamW.step() |
Tensor |
Adam.step() |
Tensor |
Adagrad.step() |
Tensor |
Optimizer.step(Pointer closure)
A loss function closure, which is expected to return the loss value.
|
Tensor |
SGD.step(torch.LossClosure closure) |
Tensor |
RMSprop.step(torch.LossClosure closure) |
Tensor |
LBFGS.step(torch.LossClosure closure) |
Tensor |
AdamW.step(torch.LossClosure closure) |
Tensor |
Adam.step(torch.LossClosure closure) |
Tensor |
Adagrad.step(torch.LossClosure closure) |
Tensor |
Tensor.stft(long n_fft) |
Tensor |
Tensor.stft(long n_fft,
LongOptional hop_length,
LongOptional win_length,
TensorOptional window,
boolean normalized,
BoolOptional onesided,
BoolOptional return_complex) |
Tensor |
Tensor.sub_(Scalar other) |
Tensor |
Tensor.sub_(Scalar other,
Scalar alpha) |
Tensor |
Tensor.sub_(Tensor other) |
Tensor |
Tensor.sub_(Tensor other,
Scalar alpha) |
Tensor |
Tensor.sub(Scalar other) |
Tensor |
Tensor.sub(Scalar other,
Scalar alpha) |
Tensor |
Tensor.sub(Tensor other) |
Tensor |
Tensor.sub(Tensor other,
Scalar alpha) |
Tensor |
Tensor.subtract_(Scalar other) |
Tensor |
Tensor.subtract_(Scalar other,
Scalar alpha) |
Tensor |
Tensor.subtract_(Tensor other) |
Tensor |
Tensor.subtract_(Tensor other,
Scalar alpha) |
Tensor |
Tensor.subtract() |
Tensor |
Tensor.subtract(Scalar other) |
Tensor |
Tensor.subtract(Scalar other,
Scalar alpha) |
Tensor |
Tensor.subtract(Tensor other) |
Tensor |
Tensor.subtract(Tensor other,
Scalar alpha) |
Tensor |
Tensor.subtractPut(Scalar other) |
Tensor |
Tensor.subtractPut(Tensor other) |
Tensor |
Tensor.sum_to_size(long... size) |
Tensor |
Tensor.sum_to_size(LongArrayRef size) |
Tensor |
Tensor.sum() |
Tensor |
AdagradParamState.sum() |
Tensor |
Tensor.sum(DimnameArrayRef dim) |
Tensor |
Tensor.sum(DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.sum(long... dim) |
Tensor |
Tensor.sum(long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.sum(LongArrayRef dim) |
Tensor |
Tensor.sum(LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
Tensor |
Tensor.sum(ScalarTypeOptional dtype) |
Tensor |
Tensor.swapaxes_(long axis0,
long axis1) |
Tensor |
Tensor.swapaxes(long axis0,
long axis1) |
Tensor |
Tensor.swapdims_(long dim0,
long dim1) |
Tensor |
Tensor.swapdims(long dim0,
long dim1) |
Tensor |
Tensor.t_() |
Tensor |
Tensor.t() |
Tensor |
JitNode.t(Symbol name) |
Tensor |
Tensor.take_along_dim(Tensor indices) |
Tensor |
Tensor.take_along_dim(Tensor indices,
LongOptional dim) |
Tensor |
Tensor.take(Tensor index) |
Tensor |
Tensor.tan_() |
Tensor |
Tensor.tan() |
Tensor |
Tensor.tanh_() |
Tensor |
Tensor.tanh() |
Tensor |
Example.target() |
Tensor |
MNIST.targets()
Returns all targets stacked into a single tensor.
|
Tensor |
Tensor.tensor_data()
NOTE: This is similar to the legacy
.data() function on Variable, and is intended
to be used from functions that need to access the Variable's equivalent Tensor
(i.e. |
Tensor |
TensorIndex.tensor() |
Tensor |
TensorArg.tensor() |
Tensor |
Tensor.tile(long... dims) |
Tensor |
Tensor.tile(LongArrayRef dims) |
Tensor |
Tensor.to_dense() |
Tensor |
Tensor.to_dense(ScalarTypeOptional dtype) |
Tensor |
Tensor.to_mkldnn() |
Tensor |
Tensor.to_mkldnn(ScalarTypeOptional dtype) |
Tensor |
Tensor.to_sparse() |
Tensor |
Tensor.to_sparse(long sparse_dim) |
Tensor |
Tensor.to() |
Tensor |
Tensor.to(Device device,
torch.ScalarType dtype) |
Tensor |
Tensor.to(Device device,
torch.ScalarType dtype,
boolean non_blocking,
boolean copy,
MemoryFormatOptional memory_format) |
Tensor |
Tensor.to(Device device,
TypeMeta type_meta) |
Tensor |
Tensor.to(Device device,
TypeMeta type_meta,
boolean non_blocking,
boolean copy) |
Tensor |
Tensor.to(ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
boolean non_blocking,
boolean copy,
MemoryFormatOptional memory_format) |
Tensor |
Tensor.to(Tensor other) |
Tensor |
Tensor.to(Tensor other,
boolean non_blocking,
boolean copy,
MemoryFormatOptional memory_format) |
Tensor |
Tensor.to(TensorOptions options,
boolean non_blocking,
boolean copy,
MemoryFormatOptional memory_format) |
Tensor |
Tensor.to(torch.ScalarType dtype) |
Tensor |
Tensor.to(torch.ScalarType dtype,
boolean non_blocking,
boolean copy,
MemoryFormatOptional memory_format) |
Tensor |
Tensor.to(TypeMeta type_meta) |
Tensor |
Tensor.to(TypeMeta type_meta,
boolean non_blocking,
boolean copy) |
Tensor |
Tensor.toBackend(int b) |
Tensor |
Tensor.toBackend(torch.Backend b) |
Tensor |
IValue.toTensor() |
Tensor |
Tensor.toType(torch.ScalarType t) |
Tensor |
Tensor.trace() |
Tensor |
Tensor.transpose_(long dim0,
long dim1) |
Tensor |
Tensor.transpose(Dimname dim0,
Dimname dim1) |
Tensor |
Tensor.transpose(long dim0,
long dim1) |
Tensor |
Tensor.tril_() |
Tensor |
Tensor.tril_(long diagonal) |
Tensor |
Tensor.tril() |
Tensor |
Tensor.tril(long diagonal) |
Tensor |
Tensor.triu_() |
Tensor |
Tensor.triu_(long diagonal) |
Tensor |
Tensor.triu() |
Tensor |
Tensor.triu(long diagonal) |
Tensor |
Tensor.true_divide_(Scalar other) |
Tensor |
Tensor.true_divide_(Tensor other) |
Tensor |
Tensor.true_divide(Scalar other) |
Tensor |
Tensor.true_divide(Tensor other) |
Tensor |
Tensor.trunc_() |
Tensor |
Tensor.trunc() |
Tensor |
Tensor.type_as(Tensor other) |
static Tensor |
ForwardGrad.undef_grad() |
Tensor |
AutogradMetaFactory.undefined_tensor() |
Tensor |
Tensor.unflatten(Dimname dim,
long[] sizes,
DimnameArrayRef names) |
Tensor |
Tensor.unflatten(Dimname dim,
LongArrayRef sizes,
DimnameArrayRef names) |
Tensor |
Tensor.unflatten(long dim,
long... sizes) |
Tensor |
Tensor.unflatten(long dim,
long[] sizes,
DimnameListOptional names) |
Tensor |
Tensor.unflatten(long dim,
LongArrayRef sizes) |
Tensor |
Tensor.unflatten(long dim,
LongArrayRef sizes,
DimnameListOptional names) |
Tensor |
Tensor.unfold(long dimension,
long size,
long step) |
Tensor |
Tensor.uniform_() |
Tensor |
Tensor.uniform_(double from,
double to,
GeneratorOptional generator) |
Tensor |
SavedVariable.unpack() |
Tensor |
SavedVariable.unpack(Node saved_for)
Reconstructs the saved variable.
|
Tensor |
PackedSequence.unsorted_indices() |
Tensor |
Tensor.unsqueeze_(long dim) |
Tensor |
Tensor.unsqueeze(long dim) |
Tensor |
MultiheadAttentionImpl.v_proj_weight() |
Tensor |
MultiheadAttentionForwardFuncOptions.v_proj_weight() |
Tensor |
NamedTensor.value() |
Tensor |
ForwardGrad.value(long level) |
Tensor |
Tensor.values() |
Tensor |
ParameterDictImpl.values()
Return the Values in the dict
|
Tensor |
Tensor.var() |
Tensor |
Tensor.var(boolean unbiased) |
Tensor |
Tensor.var(DimnameArrayRef dim) |
Tensor |
Tensor.var(DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
Tensor |
Tensor.var(DimnameArrayRef dim,
LongOptional correction) |
Tensor |
Tensor.var(DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
Tensor |
Tensor.var(int dim) |
Tensor |
Tensor.var(long... dim) |
Tensor |
Tensor.var(long[] dim,
boolean unbiased,
boolean keepdim) |
Tensor |
Tensor.var(LongArrayRef dim) |
Tensor |
Tensor.var(LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
Tensor |
Tensor.var(LongArrayRefOptional dim,
LongOptional correction) |
Tensor |
Tensor.var(LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
Tensor |
Tensor.variable_data()
NOTE:
var.variable_data() in C++ has the same semantics as tensor.data
in Python, which create a new Variable that shares the same storage and
tensor metadata with the original Variable, but with a completely new
autograd history. |
Tensor |
Tensor.vdot(Tensor other) |
Tensor |
Tensor.ve() |
Tensor |
Tensor.view_as(Tensor other) |
Tensor |
Tensor.view(long... size) |
Tensor |
Tensor.view(LongArrayRef size) |
Tensor |
Tensor.view(torch.ScalarType dtype) |
Tensor |
Tensor.vulkan() |
Tensor |
RNNCellImplBase.weight_hh() |
Tensor |
LSTMCellImplBase.weight_hh() |
Tensor |
GRUCellImplBase.weight_hh() |
Tensor |
RNNCellImplBase.weight_ih() |
Tensor |
LSTMCellImplBase.weight_ih() |
Tensor |
GRUCellImplBase.weight_ih() |
Tensor |
PReLUImpl.weight()
The learned weight.
|
Tensor |
NLLLossOptions.weight() |
Tensor |
NLLLossImpl.weight()
A manual rescaling weight given to to each class.
|
Tensor |
MultiMarginLossOptions.weight() |
Tensor |
MultiLabelSoftMarginLossOptions.weight() |
Tensor |
LinearImpl.weight()
The learned weight.
|
Tensor |
LayerNormImpl.weight()
The learned weight.
|
Tensor |
LayerNormFuncOptions.weight() |
Tensor |
InstanceNormFuncOptions.weight() |
Tensor |
InstanceNorm3dImplBaseBase.weight()
The learned weight.
|
Tensor |
InstanceNorm2dImplBaseBase.weight()
The learned weight.
|
Tensor |
InstanceNorm1dImplBaseBase.weight()
The learned weight.
|
Tensor |
GroupNormImpl.weight()
The learned weight.
|
Tensor |
GroupNormFuncOptions.weight() |
Tensor |
EmbeddingImpl.weight()
The embedding table.
|
Tensor |
EmbeddingBagImpl.weight()
The embedding table.
|
Tensor |
CrossEntropyLossOptions.weight() |
Tensor |
CrossEntropyLossImpl.weight()
A manual rescaling weight given to to each class.
|
Tensor |
ConvTranspose3dImplBaseBase.weight()
The learned kernel (or "weight").
|
Tensor |
ConvTranspose2dImplBaseBase.weight()
The learned kernel (or "weight").
|
Tensor |
ConvTranspose1dImplBaseBase.weight()
The learned kernel (or "weight").
|
Tensor |
Conv3dImplBase.weight()
The learned kernel (or "weight").
|
Tensor |
Conv2dImplBase.weight()
The learned kernel (or "weight").
|
Tensor |
Conv1dImplBase.weight()
The learned kernel (or "weight").
|
Tensor |
BilinearImpl.weight()
The learned weight.
|
Tensor |
BatchNormFuncOptions.weight() |
Tensor |
BatchNorm3dImplBaseBase.weight()
The learned weight.
|
Tensor |
BatchNorm2dImplBaseBase.weight()
The learned weight.
|
Tensor |
BatchNorm1dImplBaseBase.weight()
The learned weight.
|
Tensor |
BCEWithLogitsLossOptions.weight() |
Tensor |
BCEWithLogitsLossImpl.weight()
A manual rescaling weight given to the loss of each batch element.
|
Tensor |
BCELossOptions.weight() |
Tensor |
Tensor.where(Tensor condition,
Tensor other) |
Tensor |
Tensor.xlogy_(Scalar other) |
Tensor |
Tensor.xlogy_(Tensor other) |
Tensor |
Tensor.xlogy(Scalar other) |
Tensor |
Tensor.xlogy(Tensor other) |
Tensor |
Tensor.xorPut(Tensor other) |
Tensor |
Tensor.zero_() |
Tensor |
InputMetadata.zeros_like() |
Tensor |
VariableInfo.zeros(OptionalDeviceGuard device_guard) |
| Modifier and Type | Method and Description |
|---|---|
Tensor |
Tensor.__and__(Tensor other) |
void |
Tensor.__dispatch_set_data(Tensor new_data) |
Tensor |
Tensor.__iand__(Tensor other) |
Tensor |
Tensor.__ilshift__(Tensor other) |
Tensor |
Tensor.__ior__(Tensor other) |
Tensor |
Tensor.__irshift__(Tensor other) |
Tensor |
Tensor.__ixor__(Tensor other) |
Tensor |
Tensor.__lshift__(Tensor other) |
Tensor |
Tensor.__or__(Tensor other) |
Tensor |
Tensor.__rshift__(Tensor other) |
Tensor |
Tensor.__xor__(Tensor other) |
Tensor |
AdaptiveLogSoftmaxWithLossImpl._get_full_log_prob(Tensor input,
Tensor head_output)
Given input tensor, and output of
head, computes the log of the full distribution |
FractionalMaxPool3dImpl |
FractionalMaxPool3dImpl._random_samples(Tensor setter) |
FractionalMaxPool2dImpl |
FractionalMaxPool2dImpl._random_samples(Tensor setter) |
Tensor |
Tensor.add_(Tensor other) |
Tensor |
Tensor.add_(Tensor other,
Scalar alpha) |
int |
Node.add_input_metadata(Tensor t) |
Tensor |
Tensor.add(Tensor other) |
Tensor |
Tensor.add(Tensor other,
Scalar alpha) |
Tensor |
Tensor.addbmm_(Tensor batch1,
Tensor batch2) |
Tensor |
Tensor.addbmm_(Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addbmm(Tensor batch1,
Tensor batch2) |
Tensor |
Tensor.addbmm(Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addcdiv_(Tensor tensor1,
Tensor tensor2) |
Tensor |
Tensor.addcdiv_(Tensor tensor1,
Tensor tensor2,
Scalar value) |
Tensor |
Tensor.addcdiv(Tensor tensor1,
Tensor tensor2) |
Tensor |
Tensor.addcdiv(Tensor tensor1,
Tensor tensor2,
Scalar value) |
Tensor |
Tensor.addcmul_(Tensor tensor1,
Tensor tensor2) |
Tensor |
Tensor.addcmul_(Tensor tensor1,
Tensor tensor2,
Scalar value) |
Tensor |
Tensor.addcmul(Tensor tensor1,
Tensor tensor2) |
Tensor |
Tensor.addcmul(Tensor tensor1,
Tensor tensor2,
Scalar value) |
Tensor |
Tensor.addmm_(Tensor mat1,
Tensor mat2) |
Tensor |
Tensor.addmm_(Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addmm(Tensor mat1,
Tensor mat2) |
Tensor |
Tensor.addmm(Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addmv_(Tensor mat,
Tensor vec) |
Tensor |
Tensor.addmv_(Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addmv(Tensor mat,
Tensor vec) |
Tensor |
Tensor.addmv(Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addPut(Tensor other) |
Tensor |
Tensor.addr_(Tensor vec1,
Tensor vec2) |
Tensor |
Tensor.addr_(Tensor vec1,
Tensor vec2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.addr(Tensor vec1,
Tensor vec2) |
Tensor |
Tensor.addr(Tensor vec1,
Tensor vec2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.align_as(Tensor other) |
boolean |
Tensor.allclose(Tensor other) |
boolean |
Tensor.allclose(Tensor other,
double rtol,
double atol,
boolean equal_nan) |
Tensor |
Tensor.andPut(Tensor other) |
void |
ParameterListImpl.append(Tensor param)
push the a given parameter at the end of the list
|
Tensor |
Tensor.atan2_(Tensor other) |
Tensor |
Tensor.atan2(Tensor other) |
void |
Tensor.backward(Tensor gradient,
BoolOptional retain_graph,
boolean create_graph,
TensorListOptional inputs)
\fn bool is_leaf() const;
All Tensors that have
requires_grad() which is false will be leaf Tensors by convention. |
Tensor |
Tensor.baddbmm_(Tensor batch1,
Tensor batch2) |
Tensor |
Tensor.baddbmm_(Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.baddbmm(Tensor batch1,
Tensor batch2) |
Tensor |
Tensor.baddbmm(Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.bernoulli_(Tensor p) |
Tensor |
Tensor.bernoulli_(Tensor p,
GeneratorOptional generator) |
RNNCellImplBase |
RNNCellImplBase.bias_hh(Tensor setter) |
LSTMCellImplBase |
LSTMCellImplBase.bias_hh(Tensor setter) |
GRUCellImplBase |
GRUCellImplBase.bias_hh(Tensor setter) |
RNNCellImplBase |
RNNCellImplBase.bias_ih(Tensor setter) |
LSTMCellImplBase |
LSTMCellImplBase.bias_ih(Tensor setter) |
GRUCellImplBase |
GRUCellImplBase.bias_ih(Tensor setter) |
MultiheadAttentionImpl |
MultiheadAttentionImpl.bias_k(Tensor setter) |
MultiheadAttentionImpl |
MultiheadAttentionImpl.bias_v(Tensor setter) |
LinearImpl |
LinearImpl.bias(Tensor setter) |
LayerNormImpl |
LayerNormImpl.bias(Tensor setter) |
InstanceNorm3dImplBaseBase |
InstanceNorm3dImplBaseBase.bias(Tensor setter) |
InstanceNorm2dImplBaseBase |
InstanceNorm2dImplBaseBase.bias(Tensor setter) |
InstanceNorm1dImplBaseBase |
InstanceNorm1dImplBaseBase.bias(Tensor setter) |
GroupNormImpl |
GroupNormImpl.bias(Tensor setter) |
ConvTranspose3dImplBaseBase |
ConvTranspose3dImplBaseBase.bias(Tensor setter) |
ConvTranspose2dImplBaseBase |
ConvTranspose2dImplBaseBase.bias(Tensor setter) |
ConvTranspose1dImplBaseBase |
ConvTranspose1dImplBaseBase.bias(Tensor setter) |
Conv3dImplBase |
Conv3dImplBase.bias(Tensor setter) |
Conv2dImplBase |
Conv2dImplBase.bias(Tensor setter) |
Conv1dImplBase |
Conv1dImplBase.bias(Tensor setter) |
BilinearImpl |
BilinearImpl.bias(Tensor setter) |
BatchNorm3dImplBaseBase |
BatchNorm3dImplBaseBase.bias(Tensor setter) |
BatchNorm2dImplBaseBase |
BatchNorm2dImplBaseBase.bias(Tensor setter) |
BatchNorm1dImplBaseBase |
BatchNorm1dImplBaseBase.bias(Tensor setter) |
Tensor |
Tensor.bitwise_and_(Tensor other) |
Tensor |
Tensor.bitwise_and(Tensor other) |
Tensor |
Tensor.bitwise_left_shift_(Tensor other) |
Tensor |
Tensor.bitwise_left_shift(Tensor other) |
Tensor |
Tensor.bitwise_or_(Tensor other) |
Tensor |
Tensor.bitwise_or(Tensor other) |
Tensor |
Tensor.bitwise_right_shift_(Tensor other) |
Tensor |
Tensor.bitwise_right_shift(Tensor other) |
Tensor |
Tensor.bitwise_xor_(Tensor other) |
Tensor |
Tensor.bitwise_xor(Tensor other) |
Tensor |
Tensor.bmm(Tensor mat2) |
static TensorMaybeOwned |
TensorMaybeOwned.borrowed(Tensor t) |
void |
SavedVariableHooks.call_pack_hook(Tensor tensor) |
Tensor |
Tensor.cholesky_solve(Tensor input2) |
Tensor |
Tensor.cholesky_solve(Tensor input2,
boolean upper) |
Tensor |
Tensor.clamp_max_(Tensor max) |
Tensor |
Tensor.clamp_max(Tensor max) |
Tensor |
Tensor.clamp_min_(Tensor min) |
Tensor |
Tensor.clamp_min(Tensor min) |
Tensor |
Tensor.copy_(Tensor src) |
Tensor |
Tensor.copy_(Tensor src,
boolean non_blocking) |
Tensor |
Tensor.copysign_(Tensor other) |
Tensor |
Tensor.copysign(Tensor other) |
static TensorType |
TensorType.create(Tensor t) |
Tensor |
Tensor.cross(Tensor other) |
Tensor |
Tensor.cross(Tensor other,
LongOptional dim) |
Example |
Example.data(Tensor setter) |
Tensor |
Tensor.dist(Tensor other) |
Tensor |
Tensor.dist(Tensor other,
Scalar p) |
Tensor |
Tensor.div_(Tensor other) |
Tensor |
Tensor.div_(Tensor other,
Pointer rounding_mode) |
Tensor |
Tensor.div(Tensor other) |
Tensor |
Tensor.div(Tensor other,
Pointer rounding_mode) |
Tensor |
Tensor.divide_(Tensor other) |
Tensor |
Tensor.divide_(Tensor other,
Pointer rounding_mode) |
Tensor |
Tensor.divide(Tensor other) |
Tensor |
Tensor.divide(Tensor other,
Pointer rounding_mode) |
Tensor |
Tensor.dividePut(Tensor other) |
Tensor |
Tensor.dot(Tensor tensor) |
Tensor |
Tensor.eq_(Tensor other) |
Tensor |
Tensor.eq(Tensor other) |
boolean |
Tensor.equal(Tensor other) |
Tensor |
Tensor.expand_as(Tensor other) |
Tensor |
Tensor.fill_(Tensor value) |
Tensor |
Tensor.float_power_(Tensor exponent) |
Tensor |
Tensor.float_power(Tensor exponent) |
Tensor |
Tensor.floor_divide_(Tensor other) |
Tensor |
Tensor.floor_divide(Tensor other) |
Tensor |
Tensor.fmax(Tensor other) |
Tensor |
Tensor.fmin(Tensor other) |
Tensor |
Tensor.fmod_(Tensor other) |
Tensor |
Tensor.fmod(Tensor other) |
TensorTensorTuple |
MaxPool3dImpl.forward_with_indices(Tensor input)
Returns the outputs and the indices of the max values.
|
TensorTensorTuple |
MaxPool2dImpl.forward_with_indices(Tensor input)
Returns the outputs and the indices of the max values.
|
TensorTensorTuple |
MaxPool1dImpl.forward_with_indices(Tensor input)
Returns the outputs and the indices of the max values.
|
TensorTensorTuple |
FractionalMaxPool3dImpl.forward_with_indices(Tensor input)
Returns the outputs and the indices of the max values.
|
TensorTensorTuple |
FractionalMaxPool2dImpl.forward_with_indices(Tensor input)
Returns the outputs and the indices of the max values.
|
TensorTensorTuple |
AdaptiveMaxPool3dImpl.forward_with_indices(Tensor input)
Returns the indices along with the outputs.
|
TensorTensorTuple |
AdaptiveMaxPool2dImpl.forward_with_indices(Tensor input)
Returns the indices along with the outputs.
|
TensorTensorTuple |
AdaptiveMaxPool1dImpl.forward_with_indices(Tensor input)
Returns the indices along with the outputs.
|
PackedSequenceTensorTuple |
RNNImpl.forward_with_packed_input(PackedSequence packed_input,
Tensor hx) |
PackedSequenceTensorTuple |
GRUImpl.forward_with_packed_input(PackedSequence packed_input,
Tensor hx) |
Tensor |
ZeroPad2dImpl.forward(Tensor input) |
Tensor |
UpsampleImpl.forward(Tensor input) |
Tensor |
UnfoldImpl.forward(Tensor input) |
Tensor |
UnflattenImpl.forward(Tensor input)
Applies an unflatten transform on the
input. |
Tensor |
TransformerEncoderLayerImpl.forward(Tensor src) |
Tensor |
TransformerEncoderImpl.forward(Tensor src) |
Tensor |
ThresholdImpl.forward(Tensor input) |
Tensor |
TanhshrinkImpl.forward(Tensor input) |
Tensor |
TanhImpl.forward(Tensor input) |
Tensor |
SoftsignImpl.forward(Tensor input) |
Tensor |
SoftshrinkImpl.forward(Tensor input) |
Tensor |
SoftplusImpl.forward(Tensor input) |
Tensor |
SoftminImpl.forward(Tensor input) |
Tensor |
SoftmaxImpl.forward(Tensor input) |
Tensor |
Softmax2dImpl.forward(Tensor input) |
Tensor |
SigmoidImpl.forward(Tensor input) |
Tensor |
SiLUImpl.forward(Tensor input) |
Tensor |
SELUImpl.forward(Tensor input) |
Tensor |
ReplicationPad3dImplBase.forward(Tensor input) |
Tensor |
ReplicationPad2dImplBase.forward(Tensor input) |
Tensor |
ReplicationPad1dImplBase.forward(Tensor input) |
Tensor |
ReflectionPad3dImplBase.forward(Tensor input) |
Tensor |
ReflectionPad2dImplBase.forward(Tensor input) |
Tensor |
ReflectionPad1dImplBase.forward(Tensor input) |
Tensor |
ReLUImpl.forward(Tensor input) |
Tensor |
ReLU6Impl.forward(Tensor input) |
Tensor |
RReLUImpl.forward(Tensor input) |
TensorTensorTuple |
RNNImpl.forward(Tensor input) |
Tensor |
RNNCellImpl.forward(Tensor input) |
Tensor |
PixelUnshuffleImpl.forward(Tensor input) |
Tensor |
PixelShuffleImpl.forward(Tensor input) |
Tensor |
PReLUImpl.forward(Tensor input) |
Tensor |
MishImpl.forward(Tensor input) |
Tensor |
MaxPool3dImpl.forward(Tensor input) |
Tensor |
MaxPool2dImpl.forward(Tensor input) |
Tensor |
MaxPool1dImpl.forward(Tensor input) |
Tensor |
LogSoftmaxImpl.forward(Tensor input) |
Tensor |
LogSigmoidImpl.forward(Tensor input) |
Tensor |
LocalResponseNormImpl.forward(Tensor input) |
Tensor |
LinearImpl.forward(Tensor input)
Transforms the
input tensor by multiplying with the weight and
optionally adding the bias, if with_bias is true in the options. |
Tensor |
LeakyReLUImpl.forward(Tensor input) |
Tensor |
LayerNormImpl.forward(Tensor input)
Applies layer normalization over a mini-batch of inputs as described in
the paper
Layer Normalization_ . |
TensorTensorTensorTupleTuple |
LSTMImpl.forward(Tensor input) |
TensorTensorTuple |
LSTMCellImpl.forward(Tensor input) |
Tensor |
LPPool2dImpl.forward(Tensor input) |
Tensor |
LPPool1dImpl.forward(Tensor input) |
Tensor |
InstanceNorm3dImplBase.forward(Tensor input) |
Tensor |
InstanceNorm2dImplBase.forward(Tensor input) |
Tensor |
InstanceNorm1dImplBase.forward(Tensor input) |
Tensor |
IdentityImpl.forward(Tensor input) |
Tensor |
HardtanhImpl.forward(Tensor input) |
Tensor |
HardshrinkImpl.forward(Tensor input) |
Tensor |
GroupNormImpl.forward(Tensor input) |
TensorTensorTuple |
GRUImpl.forward(Tensor input) |
Tensor |
GRUCellImpl.forward(Tensor input) |
Tensor |
GLUImpl.forward(Tensor input) |
Tensor |
GELUImpl.forward(Tensor input) |
Tensor |
FractionalMaxPool3dImpl.forward(Tensor input) |
Tensor |
FractionalMaxPool2dImpl.forward(Tensor input) |
Tensor |
FoldImpl.forward(Tensor input) |
Tensor |
FlattenImpl.forward(Tensor input)
Applies a flatten transform on the
input. |
Tensor |
FeatureAlphaDropoutImpl.forward(Tensor input) |
Tensor |
EmbeddingImpl.forward(Tensor indices)
Performs a lookup on the embedding table stored in
weight using the
indices supplied and returns the result. |
Tensor |
EmbeddingBagImpl.forward(Tensor input) |
Tensor |
ELUImpl.forward(Tensor input) |
Tensor |
DropoutImpl.forward(Tensor input) |
Tensor |
Dropout3dImpl.forward(Tensor input) |
Tensor |
Dropout2dImpl.forward(Tensor input) |
Tensor |
CrossMapLRN2dImpl.forward(Tensor input) |
Tensor |
ConvTranspose3dImpl.forward(Tensor input) |
Tensor |
ConvTranspose2dImpl.forward(Tensor input) |
Tensor |
ConvTranspose1dImpl.forward(Tensor input) |
Tensor |
Conv3dImpl.forward(Tensor input) |
Tensor |
Conv2dImpl.forward(Tensor input) |
Tensor |
Conv1dImpl.forward(Tensor input) |
Tensor |
ConstantPad3dImplBase.forward(Tensor input) |
Tensor |
ConstantPad2dImplBase.forward(Tensor input) |
Tensor |
ConstantPad1dImplBase.forward(Tensor input) |
Tensor |
CELUImpl.forward(Tensor input) |
Tensor |
BatchNorm3dImplBase.forward(Tensor input) |
Tensor |
BatchNorm2dImplBase.forward(Tensor input) |
Tensor |
BatchNorm1dImplBase.forward(Tensor input) |
Tensor |
AvgPool3dImpl.forward(Tensor input) |
Tensor |
AvgPool2dImpl.forward(Tensor input) |
Tensor |
AvgPool1dImpl.forward(Tensor input) |
Tensor |
AlphaDropoutImpl.forward(Tensor input) |
Tensor |
AdaptiveMaxPool3dImpl.forward(Tensor input) |
Tensor |
AdaptiveMaxPool2dImpl.forward(Tensor input) |
Tensor |
AdaptiveMaxPool1dImpl.forward(Tensor input) |
Tensor |
AdaptiveAvgPool3dImpl.forward(Tensor input) |
Tensor |
AdaptiveAvgPool2dImpl.forward(Tensor input) |
Tensor |
AdaptiveAvgPool1dImpl.forward(Tensor input) |
Tensor |
ConvTranspose3dImpl.forward(Tensor input,
LongArrayRefOptional output_size) |
Tensor |
ConvTranspose2dImpl.forward(Tensor input,
LongArrayRefOptional output_size) |
Tensor |
ConvTranspose1dImpl.forward(Tensor input,
LongArrayRefOptional output_size) |
Tensor |
TransformerImpl.forward(Tensor src,
Tensor tgt) |
Tensor |
TransformerDecoderLayerImpl.forward(Tensor tgt,
Tensor memory) |
Tensor |
TransformerDecoderImpl.forward(Tensor tgt,
Tensor memory) |
Tensor |
SoftMarginLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
SmoothL1LossImpl.forward(Tensor input,
Tensor target) |
TensorTensorTuple |
RNNImpl.forward(Tensor input,
Tensor hx) |
Tensor |
RNNCellImpl.forward(Tensor input,
Tensor hx) |
Tensor |
PoissonNLLLossImpl.forward(Tensor log_input,
Tensor targets) |
Tensor |
PairwiseDistanceImpl.forward(Tensor input1,
Tensor input2) |
Tensor |
NLLLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
MultiMarginLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
MultiLabelSoftMarginLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
MultiLabelMarginLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
MaxUnpool3dImpl.forward(Tensor input,
Tensor indices) |
Tensor |
MaxUnpool2dImpl.forward(Tensor input,
Tensor indices) |
Tensor |
MaxUnpool1dImpl.forward(Tensor input,
Tensor indices) |
Tensor |
MSELossImpl.forward(Tensor input,
Tensor target) |
Tensor |
L1LossImpl.forward(Tensor input,
Tensor target) |
Tensor |
KLDivLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
HuberLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
HingeEmbeddingLossImpl.forward(Tensor input,
Tensor target) |
TensorTensorTuple |
GRUImpl.forward(Tensor input,
Tensor hx) |
Tensor |
GRUCellImpl.forward(Tensor input,
Tensor hx) |
Tensor |
CrossEntropyLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
CosineSimilarityImpl.forward(Tensor input1,
Tensor input2) |
Tensor |
BilinearImpl.forward(Tensor input1,
Tensor input2)
Applies a bilinear transform on the
input1 and input2 tensor by multiplying
with the weight and optionally adding the bias, if with_bias
is true in the options. |
Tensor |
BCEWithLogitsLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
BCELossImpl.forward(Tensor input,
Tensor target) |
ASMoutput |
AdaptiveLogSoftmaxWithLossImpl.forward(Tensor input,
Tensor target) |
Tensor |
MaxUnpool3dImpl.forward(Tensor input,
Tensor indices,
LongVectorOptional output_size) |
Tensor |
MaxUnpool2dImpl.forward(Tensor input,
Tensor indices,
LongVectorOptional output_size) |
Tensor |
MaxUnpool1dImpl.forward(Tensor input,
Tensor indices,
LongVectorOptional output_size) |
Tensor |
TripletMarginWithDistanceLossImpl.forward(Tensor anchor,
Tensor positive,
Tensor negative) |
Tensor |
TripletMarginLossImpl.forward(Tensor anchor,
Tensor positive,
Tensor negative) |
Tensor |
TransformerEncoderLayerImpl.forward(Tensor src,
Tensor src_mask,
Tensor src_key_padding_mask) |
Tensor |
TransformerEncoderImpl.forward(Tensor src,
Tensor src_mask,
Tensor src_key_padding_mask) |
TensorTensorTuple |
MultiheadAttentionImpl.forward(Tensor query,
Tensor key,
Tensor value) |
Tensor |
MarginRankingLossImpl.forward(Tensor input1,
Tensor input2,
Tensor targets) |
Tensor |
EmbeddingBagImpl.forward(Tensor input,
Tensor offsets,
Tensor per_sample_weights) |
Tensor |
CosineEmbeddingLossImpl.forward(Tensor input1,
Tensor input2,
Tensor target) |
TensorTensorTensorTupleTuple |
LSTMImpl.forward(Tensor input,
TensorTensorOptional hx_opt) |
TensorTensorTuple |
LSTMCellImpl.forward(Tensor input,
TensorTensorOptional hx_opt) |
Tensor |
CTCLossImpl.forward(Tensor log_probs,
Tensor targets,
Tensor input_lengths,
Tensor target_lengths) |
TensorTensorTuple |
MultiheadAttentionImpl.forward(Tensor query,
Tensor key,
Tensor value,
Tensor key_padding_mask,
boolean need_weights,
Tensor attn_mask) |
Tensor |
TransformerDecoderLayerImpl.forward(Tensor tgt,
Tensor memory,
Tensor tgt_mask,
Tensor memory_mask,
Tensor tgt_key_padding_mask,
Tensor memory_key_padding_mask)
Pass the inputs (and mask) through the decoder layer.
|
Tensor |
TransformerDecoderImpl.forward(Tensor tgt,
Tensor memory,
Tensor tgt_mask,
Tensor memory_mask,
Tensor tgt_key_padding_mask,
Tensor memory_key_padding_mask)
Pass the inputs (and mask) through the decoder layer in turn.
|
Tensor |
TransformerImpl.forward(Tensor src,
Tensor tgt,
Tensor src_mask,
Tensor tgt_mask,
Tensor memory_mask,
Tensor src_key_padding_mask,
Tensor tgt_key_padding_mask,
Tensor memory_key_padding_mask)
forward function for Transformer Module
Args:
src: the sequence to the encoder (required).
|
static EmbeddingBag |
EmbeddingBag.from_pretrained(Tensor embeddings) |
static Embedding |
Embedding.from_pretrained(Tensor embeddings) |
static EmbeddingBag |
EmbeddingBag.from_pretrained(Tensor embeddings,
EmbeddingBagFromPretrainedOptions options)
See the documentation for
torch::nn::EmbeddingBagFromPretrainedOptions class to learn what
optional arguments are supported for this function. |
static Embedding |
Embedding.from_pretrained(Tensor embeddings,
EmbeddingFromPretrainedOptions options)
See the documentation for
torch::nn::EmbeddingFromPretrainedOptions class to learn what
optional arguments are supported for this function. |
Tensor |
Tensor.gather(Dimname dim,
Tensor index) |
Tensor |
Tensor.gather(Dimname dim,
Tensor index,
boolean sparse_grad) |
Tensor |
Tensor.gather(long dim,
Tensor index) |
Tensor |
Tensor.gather(long dim,
Tensor index,
boolean sparse_grad) |
Tensor |
Tensor.gcd_(Tensor other) |
Tensor |
Tensor.gcd(Tensor other) |
Tensor |
Tensor.ge_(Tensor other) |
Tensor |
Tensor.ge(Tensor other) |
Tensor |
Tensor.ger(Tensor vec2) |
Tensor |
Tensor.get(Tensor index) |
AutogradMeta |
AutogradMeta.grad_(Tensor setter) |
Tensor |
Tensor.greater_(Tensor other) |
Tensor |
Tensor.greater_equal_(Tensor other) |
Tensor |
Tensor.greater_equal(Tensor other) |
Tensor |
Tensor.greater(Tensor other) |
Tensor |
Tensor.gt_(Tensor other) |
Tensor |
Tensor.gt(Tensor other) |
Tensor |
Tensor.hardshrink_backward(Tensor grad_out,
Scalar lambd) |
Tensor |
Tensor.heaviside_(Tensor values) |
Tensor |
Tensor.heaviside(Tensor values) |
TensorTensorTuple |
Tensor.histogram(Tensor bins) |
TensorTensorTuple |
Tensor.histogram(Tensor bins,
TensorOptional weight,
boolean density) |
Tensor |
Tensor.hypot_(Tensor other) |
Tensor |
Tensor.hypot(Tensor other) |
Tensor |
Tensor.igamma_(Tensor other) |
Tensor |
Tensor.igamma(Tensor other) |
Tensor |
Tensor.igammac_(Tensor other) |
Tensor |
Tensor.igammac(Tensor other) |
MultiheadAttentionImpl |
MultiheadAttentionImpl.in_proj_bias(Tensor setter) |
MultiheadAttentionImpl |
MultiheadAttentionImpl.in_proj_weight(Tensor setter) |
Tensor |
Tensor.index_add_(long dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_add_(long dim,
Tensor index,
Tensor source,
Scalar alpha) |
Tensor |
Tensor.index_add(Dimname dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_add(Dimname dim,
Tensor index,
Tensor source,
Scalar alpha) |
Tensor |
Tensor.index_add(long dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_add(long dim,
Tensor index,
Tensor source,
Scalar alpha) |
Tensor |
Tensor.index_copy_(Dimname dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_copy_(long dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_copy(Dimname dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_copy(long dim,
Tensor index,
Tensor source) |
Tensor |
Tensor.index_fill_(Dimname dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.index_fill_(Dimname dim,
Tensor index,
Tensor value) |
Tensor |
Tensor.index_fill_(long dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.index_fill_(long dim,
Tensor index,
Tensor value) |
Tensor |
Tensor.index_fill(Dimname dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.index_fill(Dimname dim,
Tensor index,
Tensor value) |
Tensor |
Tensor.index_fill(long dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.index_fill(long dim,
Tensor index,
Tensor value) |
Tensor |
Tensor.index_put_(TensorIndexArrayRef indices,
Tensor rhs) |
Tensor |
Tensor.index_select(Dimname dim,
Tensor index) |
Tensor |
Tensor.index_select(long dim,
Tensor index) |
void |
Value.inferTypeFrom(Tensor output) |
Tensor |
Tensor.inner(Tensor other) |
Tensor |
ParameterDictImpl.insert(BytePointer key,
Tensor param)
Insert the parameter along with the key into ParameterDict
The parameter is set to be require grad by default
|
Tensor |
ParameterDictImpl.insert(String key,
Tensor param) |
TensorDeque.Iterator |
TensorDeque.insert(TensorDeque.Iterator pos,
Tensor value) |
TensorVector.Iterator |
TensorVector.insert(TensorVector.Iterator pos,
Tensor value) |
boolean |
Tensor.is_same_size(Tensor other) |
boolean |
Tensor.is_set_to(Tensor tensor) |
Tensor |
Tensor.isclose(Tensor other) |
Tensor |
Tensor.isclose(Tensor other,
double rtol,
double atol,
boolean equal_nan) |
MultiheadAttentionImpl |
MultiheadAttentionImpl.k_proj_weight(Tensor setter) |
Tensor |
Tensor.kron(Tensor other) |
Tensor |
Tensor.lcm_(Tensor other) |
Tensor |
Tensor.lcm(Tensor other) |
Tensor |
Tensor.ldexp_(Tensor other) |
Tensor |
Tensor.ldexp(Tensor other) |
Tensor |
Tensor.le_(Tensor other) |
Tensor |
Tensor.le(Tensor other) |
Tensor |
Tensor.lerp_(Tensor end,
Scalar weight) |
Tensor |
Tensor.lerp_(Tensor end,
Tensor weight) |
Tensor |
Tensor.lerp(Tensor end,
Scalar weight) |
Tensor |
Tensor.lerp(Tensor end,
Tensor weight) |
Tensor |
Tensor.less_(Tensor other) |
Tensor |
Tensor.less_equal_(Tensor other) |
Tensor |
Tensor.less_equal(Tensor other) |
Tensor |
Tensor.less(Tensor other) |
Tensor |
AdaptiveLogSoftmaxWithLossImpl.log_prob(Tensor input)
Computes log probabilities for all n_classes
|
Tensor |
Tensor.logaddexp(Tensor other) |
Tensor |
Tensor.logaddexp2(Tensor other) |
Tensor |
Tensor.logical_and_(Tensor other) |
Tensor |
Tensor.logical_and(Tensor other) |
Tensor |
Tensor.logical_or_(Tensor other) |
Tensor |
Tensor.logical_or(Tensor other) |
Tensor |
Tensor.logical_xor_(Tensor other) |
Tensor |
Tensor.logical_xor(Tensor other) |
TensorTensorTuple |
Tensor.lstsq(Tensor A) |
Tensor |
Tensor.lt_(Tensor other) |
Tensor |
Tensor.lt(Tensor other) |
Tensor |
Tensor.lu_solve(Tensor LU_data,
Tensor LU_pivots) |
Tensor |
Tensor.masked_fill_(Tensor mask,
Scalar value) |
Tensor |
Tensor.masked_fill_(Tensor mask,
Tensor value) |
Tensor |
Tensor.masked_fill(Tensor mask,
Scalar value) |
Tensor |
Tensor.masked_fill(Tensor mask,
Tensor value) |
Tensor |
Tensor.masked_scatter_(Tensor mask,
Tensor source) |
Tensor |
Tensor.masked_scatter(Tensor mask,
Tensor source) |
Tensor |
Tensor.masked_select(Tensor mask) |
boolean |
TensorType.matchTensor(Tensor t) |
Tensor |
Tensor.matmul(Tensor other) |
Tensor |
Tensor.max(Tensor other) |
Tensor |
Tensor.maximum(Tensor other) |
Tensor |
Tensor.min(Tensor other) |
Tensor |
Tensor.minimum(Tensor other) |
Tensor |
Tensor.mm(Tensor mat2) |
Tensor |
Tensor.mul_(Tensor other) |
Tensor |
Tensor.mul(Tensor other) |
Tensor |
Tensor.multiply_(Tensor other) |
Tensor |
Tensor.multiply(Tensor other) |
Tensor |
Tensor.multiplyPut(Tensor other) |
Tensor |
Tensor.mv(Tensor vec) |
Tensor |
Tensor.nanquantile(Tensor q) |
Tensor |
Tensor.nanquantile(Tensor q,
LongOptional dim,
boolean keepdim) |
Tensor |
Tensor.nanquantile(Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
Tensor |
Tensor.narrow(long dim,
Tensor start,
long length) |
Tensor |
Tensor.ne_(Tensor other) |
Tensor |
Tensor.ne(Tensor other) |
Tensor |
Tensor.nextafter_(Tensor other) |
Tensor |
Tensor.nextafter(Tensor other) |
Tensor |
Tensor.not_equal_(Tensor other) |
Tensor |
Tensor.not_equal(Tensor other) |
InstanceNorm3dImplBaseBase |
InstanceNorm3dImplBaseBase.num_batches_tracked(Tensor setter) |
InstanceNorm2dImplBaseBase |
InstanceNorm2dImplBaseBase.num_batches_tracked(Tensor setter) |
InstanceNorm1dImplBaseBase |
InstanceNorm1dImplBaseBase.num_batches_tracked(Tensor setter) |
BatchNorm3dImplBaseBase |
BatchNorm3dImplBaseBase.num_batches_tracked(Tensor setter) |
BatchNorm2dImplBaseBase |
BatchNorm2dImplBaseBase.num_batches_tracked(Tensor setter) |
BatchNorm1dImplBaseBase |
BatchNorm1dImplBaseBase.num_batches_tracked(Tensor setter) |
Tensor |
Tensor.orgqr(Tensor input2) |
Tensor |
Tensor.ormqr(Tensor input2,
Tensor input3) |
Tensor |
Tensor.ormqr(Tensor input2,
Tensor input3,
boolean left,
boolean transpose) |
Tensor |
Tensor.orPut(Tensor other) |
Tensor |
Tensor.outer(Tensor vec2) |
ASMoutput |
ASMoutput.output(Tensor setter) |
static TensorMaybeOwned |
TensorMaybeOwned.owned(Tensor t) |
BCEWithLogitsLossImpl |
BCEWithLogitsLossImpl.pos_weight(Tensor setter) |
Tensor |
Tensor.pow_(Tensor exponent) |
Tensor |
Tensor.pow(Tensor exponent) |
Tensor |
AdaptiveLogSoftmaxWithLossImpl.predict(Tensor input)
This is equivalent to
log_pob(input).argmax(1) but is more efficient in some cases |
TensorTensorTuple |
Tensor.prelu_backward(Tensor grad_output,
Tensor weight) |
Tensor |
Tensor.prelu(Tensor weight) |
TensorVector |
TensorVector.push_back(Tensor value) |
TensorDeque |
TensorDeque.push_back(Tensor value) |
Tensor |
Tensor.put_(Tensor index,
Tensor source) |
Tensor |
Tensor.put_(Tensor index,
Tensor source,
boolean accumulate) |
StringTensorPair |
StringTensorPair.put(BytePointer firstValue,
Tensor secondValue) |
StringTensorMap |
StringTensorMap.put(BytePointer i,
Tensor value) |
StringTensorDict |
StringTensorDict.put(BytePointer i,
Tensor value) |
TensorVector |
TensorVector.put(long i,
Tensor value) |
TensorDeque |
TensorDeque.put(long i,
Tensor value) |
StringTensorPair |
StringTensorPair.put(String firstValue,
Tensor secondValue) |
TensorVector |
TensorVector.put(Tensor... array) |
TensorDeque |
TensorDeque.put(Tensor... array) |
TensorVector |
TensorVector.put(Tensor value) |
TensorOptional |
TensorOptional.put(Tensor value) |
TensorDeque |
TensorDeque.put(Tensor value) |
Tensor |
Tensor.put(Tensor x) |
Tensor |
Tensor.put(Tensor index,
Tensor source) |
Tensor |
Tensor.put(Tensor index,
Tensor source,
boolean accumulate) |
MultiheadAttentionImpl |
MultiheadAttentionImpl.q_proj_weight(Tensor setter) |
Tensor |
Tensor.quantile(Tensor q) |
Tensor |
Tensor.quantile(Tensor q,
LongOptional dim,
boolean keepdim) |
Tensor |
Tensor.quantile(Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
void |
InputArchive.read(BytePointer key,
Tensor tensor) |
void |
InputArchive.read(BytePointer key,
Tensor tensor,
boolean is_buffer)
Reads a
tensor associated with a given key. |
void |
InputArchive.read(String key,
Tensor tensor) |
void |
InputArchive.read(String key,
Tensor tensor,
boolean is_buffer) |
Tensor |
Module.register_buffer(BytePointer name,
Tensor tensor)
Registers a buffer with this
Module. |
void |
JitModule.register_buffer(BytePointer name,
Tensor v) |
Tensor |
Module.register_buffer(String name,
Tensor tensor) |
void |
JitModule.register_buffer(String name,
Tensor v) |
Tensor |
Module.register_parameter(BytePointer name,
Tensor tensor) |
Tensor |
Module.register_parameter(BytePointer name,
Tensor tensor,
boolean requires_grad)
Registers a parameter with this
Module. |
void |
JitModule.register_parameter(BytePointer name,
Tensor v,
boolean is_buffer) |
Tensor |
Module.register_parameter(String name,
Tensor tensor) |
Tensor |
Module.register_parameter(String name,
Tensor tensor,
boolean requires_grad) |
void |
JitModule.register_parameter(String name,
Tensor v,
boolean is_buffer) |
Tensor |
Tensor.remainder_(Tensor other) |
Tensor |
Tensor.remainder(Tensor other) |
Tensor |
Tensor.repeat_interleave(Tensor repeats) |
Tensor |
Tensor.repeat_interleave(Tensor repeats,
LongOptional dim,
LongOptional output_size) |
Tensor |
Tensor.reshape_as(Tensor other) |
Tensor |
Tensor.resize_as_(Tensor the_template) |
Tensor |
Tensor.resize_as_(Tensor the_template,
MemoryFormatOptional memory_format) |
InstanceNorm3dImplBaseBase |
InstanceNorm3dImplBaseBase.running_mean(Tensor setter) |
InstanceNorm2dImplBaseBase |
InstanceNorm2dImplBaseBase.running_mean(Tensor setter) |
InstanceNorm1dImplBaseBase |
InstanceNorm1dImplBaseBase.running_mean(Tensor setter) |
BatchNorm3dImplBaseBase |
BatchNorm3dImplBaseBase.running_mean(Tensor setter) |
BatchNorm2dImplBaseBase |
BatchNorm2dImplBaseBase.running_mean(Tensor setter) |
BatchNorm1dImplBaseBase |
BatchNorm1dImplBaseBase.running_mean(Tensor setter) |
InstanceNorm3dImplBaseBase |
InstanceNorm3dImplBaseBase.running_var(Tensor setter) |
InstanceNorm2dImplBaseBase |
InstanceNorm2dImplBaseBase.running_var(Tensor setter) |
InstanceNorm1dImplBaseBase |
InstanceNorm1dImplBaseBase.running_var(Tensor setter) |
BatchNorm3dImplBaseBase |
BatchNorm3dImplBaseBase.running_var(Tensor setter) |
BatchNorm2dImplBaseBase |
BatchNorm2dImplBaseBase.running_var(Tensor setter) |
BatchNorm1dImplBaseBase |
BatchNorm1dImplBaseBase.running_var(Tensor setter) |
Tensor |
Tensor.scatter_(long dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.scatter_(long dim,
Tensor index,
Scalar value,
Pointer reduce) |
Tensor |
Tensor.scatter_(long dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter_(long dim,
Tensor index,
Tensor src,
Pointer reduce) |
Tensor |
Tensor.scatter_add_(long dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter_add(Dimname dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter_add(long dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter(Dimname dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.scatter(Dimname dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter(long dim,
Tensor index,
Scalar value) |
Tensor |
Tensor.scatter(long dim,
Tensor index,
Scalar value,
Pointer reduce) |
Tensor |
Tensor.scatter(long dim,
Tensor index,
Tensor src) |
Tensor |
Tensor.scatter(long dim,
Tensor index,
Tensor src,
Pointer reduce) |
StringTensorPair |
StringTensorPair.second(Tensor second) |
Tensor |
Tensor.set_(Tensor source) |
void |
Generator.set_state(Tensor new_state) |
void |
ForwardGrad.set_value(Tensor value,
long level) |
Tensor |
Tensor.smm(Tensor mat2) |
TensorTensorTuple |
Tensor.solve(Tensor A) |
Tensor |
Tensor.sparse_mask(Tensor mask) |
Tensor |
Tensor.sspaddmm(Tensor mat1,
Tensor mat2) |
Tensor |
Tensor.sspaddmm(Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
Tensor |
Tensor.sub_(Tensor other) |
Tensor |
Tensor.sub_(Tensor other,
Scalar alpha) |
Tensor |
Tensor.sub(Tensor other) |
Tensor |
Tensor.sub(Tensor other,
Scalar alpha) |
Tensor |
Tensor.subtract_(Tensor other) |
Tensor |
Tensor.subtract_(Tensor other,
Scalar alpha) |
Tensor |
Tensor.subtract(Tensor other) |
Tensor |
Tensor.subtract(Tensor other,
Scalar alpha) |
Tensor |
Tensor.subtractPut(Tensor other) |
JitNode |
JitNode.t_(Symbol name,
Tensor v) |
Tensor |
Tensor.take_along_dim(Tensor indices) |
Tensor |
Tensor.take_along_dim(Tensor indices,
LongOptional dim) |
Tensor |
Tensor.take(Tensor index) |
Example |
Example.target(Tensor setter) |
TensorVector |
Tensor.tensor_split(Tensor tensor_indices_or_sections) |
TensorVector |
Tensor.tensor_split(Tensor tensor_indices_or_sections,
long dim) |
Tensor |
Tensor.to(Tensor other) |
Tensor |
Tensor.to(Tensor other,
boolean non_blocking,
boolean copy,
MemoryFormatOptional memory_format) |
TensorTensorTuple |
Tensor.triangular_solve(Tensor A) |
TensorTensorTuple |
Tensor.triangular_solve(Tensor A,
boolean upper,
boolean transpose,
boolean unitriangular) |
Tensor |
Tensor.true_divide_(Tensor other) |
Tensor |
Tensor.true_divide(Tensor other) |
boolean |
InputArchive.try_read(BytePointer key,
Tensor tensor) |
boolean |
InputArchive.try_read(BytePointer key,
Tensor tensor,
boolean is_buffer)
Reads a
tensor associated with a given key. |
boolean |
InputArchive.try_read(String key,
Tensor tensor) |
boolean |
InputArchive.try_read(String key,
Tensor tensor,
boolean is_buffer) |
Tensor |
Tensor.type_as(Tensor other) |
MultiheadAttentionImpl |
MultiheadAttentionImpl.v_proj_weight(Tensor setter) |
NamedTensor |
NamedTensor.value(Tensor setter) |
Tensor |
Tensor.vdot(Tensor other) |
Tensor |
Tensor.view_as(Tensor other) |
RNNCellImplBase |
RNNCellImplBase.weight_hh(Tensor setter) |
LSTMCellImplBase |
LSTMCellImplBase.weight_hh(Tensor setter) |
GRUCellImplBase |
GRUCellImplBase.weight_hh(Tensor setter) |
RNNCellImplBase |
RNNCellImplBase.weight_ih(Tensor setter) |
LSTMCellImplBase |
LSTMCellImplBase.weight_ih(Tensor setter) |
GRUCellImplBase |
GRUCellImplBase.weight_ih(Tensor setter) |
PReLUImpl |
PReLUImpl.weight(Tensor setter) |
NLLLossImpl |
NLLLossImpl.weight(Tensor setter) |
LinearImpl |
LinearImpl.weight(Tensor setter) |
LayerNormImpl |
LayerNormImpl.weight(Tensor setter) |
InstanceNorm3dImplBaseBase |
InstanceNorm3dImplBaseBase.weight(Tensor setter) |
InstanceNorm2dImplBaseBase |
InstanceNorm2dImplBaseBase.weight(Tensor setter) |
InstanceNorm1dImplBaseBase |
InstanceNorm1dImplBaseBase.weight(Tensor setter) |
GroupNormImpl |
GroupNormImpl.weight(Tensor setter) |
EmbeddingImpl |
EmbeddingImpl.weight(Tensor setter) |
EmbeddingBagImpl |
EmbeddingBagImpl.weight(Tensor setter) |
CrossEntropyLossImpl |
CrossEntropyLossImpl.weight(Tensor setter) |
ConvTranspose3dImplBaseBase |
ConvTranspose3dImplBaseBase.weight(Tensor setter) |
ConvTranspose2dImplBaseBase |
ConvTranspose2dImplBaseBase.weight(Tensor setter) |
ConvTranspose1dImplBaseBase |
ConvTranspose1dImplBaseBase.weight(Tensor setter) |
Conv3dImplBase |
Conv3dImplBase.weight(Tensor setter) |
Conv2dImplBase |
Conv2dImplBase.weight(Tensor setter) |
Conv1dImplBase |
Conv1dImplBase.weight(Tensor setter) |
BilinearImpl |
BilinearImpl.weight(Tensor setter) |
BatchNorm3dImplBaseBase |
BatchNorm3dImplBaseBase.weight(Tensor setter) |
BatchNorm2dImplBaseBase |
BatchNorm2dImplBaseBase.weight(Tensor setter) |
BatchNorm1dImplBaseBase |
BatchNorm1dImplBaseBase.weight(Tensor setter) |
BCEWithLogitsLossImpl |
BCEWithLogitsLossImpl.weight(Tensor setter) |
Tensor |
Tensor.where(Tensor condition,
Tensor other) |
void |
OutputArchive.write(BytePointer key,
Tensor tensor) |
void |
OutputArchive.write(BytePointer key,
Tensor tensor,
boolean is_buffer)
Writes a
(key, tensor) pair to the OutputArchive, and marks it as
being or not being a buffer (non-differentiable tensor). |
void |
OutputArchive.write(String key,
Tensor tensor) |
void |
OutputArchive.write(String key,
Tensor tensor,
boolean is_buffer) |
Tensor |
Tensor.xlogy_(Tensor other) |
Tensor |
Tensor.xlogy(Tensor other) |
Tensor |
Tensor.xorPut(Tensor other) |
| Constructor and Description |
|---|
ASMoutput(Tensor output_,
double loss_) |
Example(Tensor data,
Tensor target) |
InputMetadata(Tensor t) |
IValue(Tensor t) |
MultiheadAttentionForwardFuncOptions(long embed_dim_to_check,
long num_heads,
Tensor in_proj_weight,
Tensor in_proj_bias,
Tensor bias_k,
Tensor bias_v,
boolean add_zero_attn,
double dropout_p,
Tensor out_proj_weight,
Tensor out_proj_bias) |
PackedSequence(Tensor data,
Tensor batch_sizes) |
PackedSequence(Tensor data,
Tensor batch_sizes,
Tensor sorted_indices,
Tensor unsorted_indices) |
SavedVariable(Tensor variable,
boolean is_output) |
SavedVariable(Tensor variable,
boolean is_output,
boolean is_inplace_on_view) |
StringTensorPair(BytePointer firstValue,
Tensor secondValue) |
StringTensorPair(String firstValue,
Tensor secondValue) |
Tensor(Tensor tensor) |
TensorArg(Tensor tensor,
BytePointer name,
int pos) |
TensorArg(Tensor tensor,
String name,
int pos) |
TensorArrayRef(Tensor OneElt)
Construct an ArrayRef from a single element.
|
TensorArrayRef(Tensor data,
long length)
Construct an ArrayRef from a pointer and length.
|
TensorArrayRef(Tensor begin,
Tensor end)
Construct an ArrayRef from a range.
|
TensorDeque(Tensor... array) |
TensorDeque(Tensor value) |
TensorGeometry(Tensor t) |
TensorIndex(Tensor tensor) |
TensorOptional(Tensor value) |
TensorTensorDoubleLongTuple(Tensor value0,
Tensor value1,
double value2,
long value3) |
TensorTensorTensorTensorLongTuple(Tensor value0,
Tensor value1,
Tensor value2,
Tensor value3,
long value4) |
TensorTensorTensorTensorTensorTuple(Tensor value0,
Tensor value1,
Tensor value2,
Tensor value3,
Tensor value4) |
TensorTensorTensorTensorTuple(Tensor value0,
Tensor value1,
Tensor value2,
Tensor value3) |
TensorTensorTensorTensorVectorTuple(Tensor value0,
Tensor value1,
Tensor value2,
TensorVector value3) |
TensorTensorTensorTuple(Tensor value0,
Tensor value1,
Tensor value2) |
TensorTensorTensorTupleTuple(Tensor value0,
TensorTensorTuple value1) |
TensorTensorTuple(Tensor value0,
Tensor value1) |
TensorTuple(Tensor value0) |
TensorVector(Tensor... array) |
TensorVector(Tensor value) |
VariableInfo(Tensor var) |
| Modifier and Type | Method and Description |
|---|---|
static Tensor |
torch.__and__(Tensor self,
Scalar other) |
static Tensor |
torch.__and__(Tensor self,
Tensor other) |
static Tensor |
torch.__dispatch_conj(Tensor self) |
static Tensor |
torch.__lshift__(Tensor self,
Scalar other) |
static Tensor |
torch.__lshift__(Tensor self,
Tensor other) |
static Tensor |
torch.__or__(Tensor self,
Scalar other) |
static Tensor |
torch.__or__(Tensor self,
Tensor other) |
static Tensor |
torch.__rshift__(Tensor self,
Scalar other) |
static Tensor |
torch.__rshift__(Tensor self,
Tensor other) |
static Tensor |
torch.__xor__(Tensor self,
Scalar other) |
static Tensor |
torch.__xor__(Tensor self,
Tensor other) |
static Tensor |
torch._adaptive_avg_pool2d_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch._adaptive_avg_pool2d(Tensor self,
long... output_size) |
static Tensor |
torch._adaptive_avg_pool2d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch._adaptive_avg_pool3d_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch._adaptive_avg_pool3d(Tensor self,
long... output_size) |
static Tensor |
torch._adaptive_avg_pool3d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch._add_batch_dim(Tensor self,
long batch_dim,
long level) |
static Tensor |
torch._add_relu_(Tensor self,
Scalar other) |
static Tensor |
torch._add_relu_(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch._add_relu_(Tensor self,
Tensor other) |
static Tensor |
torch._add_relu_(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch._add_relu_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch._add_relu_out(Tensor out,
Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch._add_relu_outf(Tensor self,
Tensor other,
Scalar alpha,
Tensor out) |
static Tensor |
torch._add_relu(Tensor self,
Scalar other) |
static Tensor |
torch._add_relu(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch._add_relu(Tensor self,
Tensor other) |
static Tensor |
torch._add_relu(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch._amp_update_scale_(Tensor self,
Tensor growth_tracker,
Tensor found_inf,
double scale_growth_factor,
double scale_backoff_factor,
long growth_interval) |
static Tensor |
torch._baddbmm_mkl_(Tensor self,
Tensor batch1,
Tensor batch2) |
static Tensor |
torch._baddbmm_mkl_(Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch._cast_Byte(Tensor self) |
static Tensor |
torch._cast_Byte(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Char(Tensor self) |
static Tensor |
torch._cast_Char(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Double(Tensor self) |
static Tensor |
torch._cast_Double(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Float(Tensor self) |
static Tensor |
torch._cast_Float(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Half(Tensor self) |
static Tensor |
torch._cast_Half(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Int(Tensor self) |
static Tensor |
torch._cast_Int(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Long(Tensor self) |
static Tensor |
torch._cast_Long(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Short(Tensor self) |
static Tensor |
torch._cast_Short(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cat_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch._cat_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch._cat_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch._cat(TensorArrayRef tensors) |
static Tensor |
torch._cat(TensorArrayRef tensors,
long dim) |
static Tensor |
torch._cdist_backward(Tensor grad,
Tensor x1,
Tensor x2,
double p,
Tensor cdist) |
static Tensor |
torch._cdist_forward(Tensor x1,
Tensor x2,
double p,
LongOptional compute_mode) |
static Tensor |
torch._cholesky_solve_helper(Tensor self,
Tensor A,
boolean upper) |
static Tensor |
torch._coalesce(Tensor self) |
static Tensor |
torch._compute_linear_combination_out(Tensor out,
Tensor input,
Tensor coefficients) |
static Tensor |
torch._compute_linear_combination_outf(Tensor input,
Tensor coefficients,
Tensor out) |
static Tensor |
torch._compute_linear_combination(Tensor input,
Tensor coefficients) |
static Tensor |
torch._conj_physical(Tensor self) |
static Tensor |
torch._conj(Tensor self) |
static Tensor |
torch._conv_depthwise2d_out(Tensor out,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long... dilation) |
static Tensor |
torch._conv_depthwise2d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static Tensor |
torch._conv_depthwise2d_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
Tensor out) |
static Tensor |
torch._conv_depthwise2d_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
Tensor out) |
static Tensor |
torch._conv_depthwise2d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long... dilation) |
static Tensor |
torch._conv_depthwise2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static Tensor |
torch._convert_indices_from_coo_to_csr_out(Tensor out,
Tensor self,
long size) |
static Tensor |
torch._convert_indices_from_coo_to_csr_out(Tensor out,
Tensor self,
long size,
boolean out_int32) |
static Tensor |
torch._convert_indices_from_coo_to_csr_outf(Tensor self,
long size,
boolean out_int32,
Tensor out) |
static Tensor |
torch._convert_indices_from_coo_to_csr(Tensor self,
long size) |
static Tensor |
torch._convert_indices_from_coo_to_csr(Tensor self,
long size,
boolean out_int32) |
static Tensor |
torch._convolution_mode(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding,
long[] dilation,
long groups) |
static Tensor |
torch._convolution_mode(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch._convolution_nogroup(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long... output_padding) |
static Tensor |
torch._convolution_nogroup(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding) |
static Tensor |
torch._convolution(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long[] output_padding,
long groups,
boolean benchmark,
boolean deterministic,
boolean cudnn_enabled) |
static Tensor |
torch._convolution(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long[] output_padding,
long groups,
boolean benchmark,
boolean deterministic,
boolean cudnn_enabled,
boolean allow_tf32) |
static Tensor |
torch._convolution(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding,
long groups,
boolean benchmark,
boolean deterministic,
boolean cudnn_enabled) |
static Tensor |
torch._convolution(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding,
long groups,
boolean benchmark,
boolean deterministic,
boolean cudnn_enabled,
boolean allow_tf32) |
static Tensor |
torch._copy_from_and_resize(Tensor self,
Tensor dst) |
static Tensor |
torch._copy_from(Tensor self,
Tensor dst) |
static Tensor |
torch._copy_from(Tensor self,
Tensor dst,
boolean non_blocking) |
static Tensor |
torch._ctc_loss_backward(Tensor grad,
Tensor log_probs,
Tensor targets,
long[] input_lengths,
long[] target_lengths,
Tensor neg_log_likelihood,
Tensor log_alpha,
long blank) |
static Tensor |
torch._ctc_loss_backward(Tensor grad,
Tensor log_probs,
Tensor targets,
long[] input_lengths,
long[] target_lengths,
Tensor neg_log_likelihood,
Tensor log_alpha,
long blank,
boolean zero_infinity) |
static Tensor |
torch._ctc_loss_backward(Tensor grad,
Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths,
Tensor neg_log_likelihood,
Tensor log_alpha,
long blank) |
static Tensor |
torch._ctc_loss_backward(Tensor grad,
Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths,
Tensor neg_log_likelihood,
Tensor log_alpha,
long blank,
boolean zero_infinity) |
static Tensor |
torch._cudnn_init_dropout_state(double dropout,
boolean train,
long dropout_seed,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._cudnn_init_dropout_state(double dropout,
boolean train,
long dropout_seed,
TensorOptions options) |
static Tensor |
torch._cudnn_rnn_flatten_weight(TensorArrayRef weight_arr,
long weight_stride0,
long input_size,
long mode,
long hidden_size,
long proj_size,
long num_layers,
boolean batch_first,
boolean bidirectional) |
static Tensor |
torch._det_lu_based_helper_backward_helper(Tensor det_grad,
Tensor det,
Tensor self,
Tensor lu,
Tensor pivs) |
static Tensor |
torch._dim_arange(Tensor like,
long dim) |
static Tensor |
torch._dirichlet_grad(Tensor x,
Tensor alpha,
Tensor total) |
static Tensor |
torch._embedding_bag_backward(Tensor grad,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
Tensor bag_size,
Tensor maximum_indices,
long num_weights,
boolean scale_grad_by_freq,
long mode,
boolean sparse,
TensorOptional per_sample_weights) |
static Tensor |
torch._embedding_bag_backward(Tensor grad,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
Tensor bag_size,
Tensor maximum_indices,
long num_weights,
boolean scale_grad_by_freq,
long mode,
boolean sparse,
TensorOptional per_sample_weights,
long padding_idx) |
static Tensor |
torch._embedding_bag_dense_backward(Tensor grad,
Tensor indices,
Tensor offset2bag,
Tensor bag_size,
Tensor maximum_indices,
long num_weights,
boolean scale_grad_by_freq,
long mode,
TensorOptional per_sample_weights) |
static Tensor |
torch._embedding_bag_dense_backward(Tensor grad,
Tensor indices,
Tensor offset2bag,
Tensor bag_size,
Tensor maximum_indices,
long num_weights,
boolean scale_grad_by_freq,
long mode,
TensorOptional per_sample_weights,
long padding_idx) |
static Tensor |
torch._embedding_bag_per_sample_weights_backward(Tensor grad,
Tensor weight,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
long mode) |
static Tensor |
torch._embedding_bag_per_sample_weights_backward(Tensor grad,
Tensor weight,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
long mode,
long padding_idx) |
static Tensor |
torch._embedding_bag_sparse_backward(Tensor grad,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
Tensor bag_size,
long num_weights,
boolean scale_grad_by_freq,
long mode,
TensorOptional per_sample_weights) |
static Tensor |
torch._embedding_bag_sparse_backward(Tensor grad,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
Tensor bag_size,
long num_weights,
boolean scale_grad_by_freq,
long mode,
TensorOptional per_sample_weights,
long padding_idx) |
static Tensor |
torch._empty_affine_quantized(long... size) |
static Tensor |
torch._empty_affine_quantized(long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
double scale,
long zero_point,
MemoryFormatOptional memory_format) |
static Tensor |
torch._empty_affine_quantized(long[] size,
TensorOptions options,
double scale,
long zero_point,
MemoryFormatOptional memory_format) |
static Tensor |
torch._empty_affine_quantized(LongArrayRef size) |
static Tensor |
torch._empty_affine_quantized(LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
double scale,
long zero_point,
MemoryFormatOptional memory_format) |
static Tensor |
torch._empty_affine_quantized(LongArrayRef size,
TensorOptions options,
double scale,
long zero_point,
MemoryFormatOptional memory_format) |
static Tensor |
torch._empty_per_channel_affine_quantized(long[] size,
Tensor scales,
Tensor zero_points,
long axis) |
static Tensor |
torch._empty_per_channel_affine_quantized(long[] size,
Tensor scales,
Tensor zero_points,
long axis,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch._empty_per_channel_affine_quantized(long[] size,
Tensor scales,
Tensor zero_points,
long axis,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch._empty_per_channel_affine_quantized(LongArrayRef size,
Tensor scales,
Tensor zero_points,
long axis) |
static Tensor |
torch._empty_per_channel_affine_quantized(LongArrayRef size,
Tensor scales,
Tensor zero_points,
long axis,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch._empty_per_channel_affine_quantized(LongArrayRef size,
Tensor scales,
Tensor zero_points,
long axis,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch._euclidean_dist(Tensor x1,
Tensor x2) |
static Tensor |
torch._fake_quantize_learnable_per_channel_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max) |
static Tensor |
torch._fake_quantize_learnable_per_channel_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max,
double grad_factor) |
static Tensor |
torch._fake_quantize_learnable_per_tensor_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long quant_min,
long quant_max) |
static Tensor |
torch._fake_quantize_learnable_per_tensor_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long quant_min,
long quant_max,
double grad_factor) |
static Tensor |
torch._fft_c2c_out(Tensor out,
Tensor self,
long[] dim,
long normalization,
boolean forward) |
static Tensor |
torch._fft_c2c_out(Tensor out,
Tensor self,
LongArrayRef dim,
long normalization,
boolean forward) |
static Tensor |
torch._fft_c2c_outf(Tensor self,
long[] dim,
long normalization,
boolean forward,
Tensor out) |
static Tensor |
torch._fft_c2c_outf(Tensor self,
LongArrayRef dim,
long normalization,
boolean forward,
Tensor out) |
static Tensor |
torch._fft_c2c(Tensor self,
long[] dim,
long normalization,
boolean forward) |
static Tensor |
torch._fft_c2c(Tensor self,
LongArrayRef dim,
long normalization,
boolean forward) |
static Tensor |
torch._fft_c2r_out(Tensor out,
Tensor self,
long[] dim,
long normalization,
long last_dim_size) |
static Tensor |
torch._fft_c2r_out(Tensor out,
Tensor self,
LongArrayRef dim,
long normalization,
long last_dim_size) |
static Tensor |
torch._fft_c2r_outf(Tensor self,
long[] dim,
long normalization,
long last_dim_size,
Tensor out) |
static Tensor |
torch._fft_c2r_outf(Tensor self,
LongArrayRef dim,
long normalization,
long last_dim_size,
Tensor out) |
static Tensor |
torch._fft_c2r(Tensor self,
long[] dim,
long normalization,
long last_dim_size) |
static Tensor |
torch._fft_c2r(Tensor self,
LongArrayRef dim,
long normalization,
long last_dim_size) |
static Tensor |
torch._fft_r2c_out(Tensor out,
Tensor self,
long[] dim,
long normalization,
boolean onesided) |
static Tensor |
torch._fft_r2c_out(Tensor out,
Tensor self,
LongArrayRef dim,
long normalization,
boolean onesided) |
static Tensor |
torch._fft_r2c_outf(Tensor self,
long[] dim,
long normalization,
boolean onesided,
Tensor out) |
static Tensor |
torch._fft_r2c_outf(Tensor self,
LongArrayRef dim,
long normalization,
boolean onesided,
Tensor out) |
static Tensor |
torch._fft_r2c(Tensor self,
long[] dim,
long normalization,
boolean onesided) |
static Tensor |
torch._fft_r2c(Tensor self,
LongArrayRef dim,
long normalization,
boolean onesided) |
static Tensor |
torch._gather_sparse_backward(Tensor self,
long dim,
Tensor index,
Tensor grad) |
static Tensor |
torch._grid_sampler_2d_cpu_fallback(Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static Tensor |
torch._index_copy_(Tensor self,
long dim,
Tensor index,
Tensor source) |
static Tensor |
torch._inverse_helper(Tensor self) |
static Tensor |
torch._linalg_inv_out_helper_(Tensor self,
Tensor infos_lu,
Tensor infos_getri) |
static Tensor |
torch._log_softmax_backward_data_out(Tensor out,
Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._log_softmax_backward_data_outf(Tensor grad_output,
Tensor output,
long dim,
Tensor self,
Tensor out) |
static Tensor |
torch._log_softmax_backward_data(Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._log_softmax_out(Tensor out,
Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._log_softmax_outf(Tensor self,
long dim,
boolean half_to_float,
Tensor out) |
static Tensor |
torch._log_softmax(Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._logcumsumexp_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch._logcumsumexp_outf(Tensor self,
long dim,
Tensor out) |
static Tensor |
torch._logcumsumexp(Tensor self,
long dim) |
static Tensor |
torch._make_dual(Tensor primal,
Tensor tangent,
long level) |
static Tensor |
torch._make_per_channel_quantized_tensor(Tensor self,
Tensor scale,
Tensor zero_point,
long axis) |
static Tensor |
torch._make_per_tensor_quantized_tensor(Tensor self,
double scale,
long zero_point) |
static Tensor |
torch._masked_scale(Tensor self,
Tensor mask,
double scale) |
static Tensor |
torch._mkldnn_reshape(Tensor self,
long... shape) |
static Tensor |
torch._mkldnn_reshape(Tensor self,
LongArrayRef shape) |
static Tensor |
torch._mkldnn_transpose_(Tensor self,
long dim0,
long dim1) |
static Tensor |
torch._mkldnn_transpose(Tensor self,
long dim0,
long dim1) |
static Tensor |
torch._narrow_with_range(Tensor input,
long dim,
long start,
long end) |
static Tensor |
torch._neg_view(Tensor self) |
static Tensor |
torch._nnpack_spatial_convolution_backward_input(Tensor input,
Tensor grad_output,
Tensor weight,
long... padding) |
static Tensor |
torch._nnpack_spatial_convolution_backward_input(Tensor input,
Tensor grad_output,
Tensor weight,
LongArrayRef padding) |
static Tensor |
torch._nnpack_spatial_convolution_backward_weight(Tensor input,
long[] weightsize,
Tensor grad_output,
long... padding) |
static Tensor |
torch._nnpack_spatial_convolution_backward_weight(Tensor input,
LongArrayRef weightsize,
Tensor grad_output,
LongArrayRef padding) |
static Tensor |
torch._nnpack_spatial_convolution(Tensor input,
Tensor weight,
TensorOptional bias,
long... padding) |
static Tensor |
torch._nnpack_spatial_convolution(Tensor input,
Tensor weight,
TensorOptional bias,
long[] padding,
long... stride) |
static Tensor |
torch._nnpack_spatial_convolution(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef padding) |
static Tensor |
torch._nnpack_spatial_convolution(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch._pack_padded_sequence_backward(Tensor grad,
long[] input_size,
Tensor batch_sizes,
boolean batch_first) |
static Tensor |
torch._pack_padded_sequence_backward(Tensor grad,
LongArrayRef input_size,
Tensor batch_sizes,
boolean batch_first) |
static Tensor |
torch._pad_circular(Tensor input,
long... padding) |
static Tensor |
torch._pad_circular(Tensor input,
LongArrayRef padding) |
static Tensor |
torch._pdist_backward(Tensor grad,
Tensor self,
double p,
Tensor pdist) |
static Tensor |
torch._pdist_forward(Tensor self) |
static Tensor |
torch._pdist_forward(Tensor self,
double p) |
static Tensor |
torch._pin_memory(Tensor self) |
static Tensor |
torch._pin_memory(Tensor self,
DeviceOptional device) |
static Tensor |
torch._remove_batch_dim(Tensor self,
long level,
long batch_size,
long out_dim) |
static Tensor |
torch._reshape_alias(Tensor self,
long[] size,
long... stride) |
static Tensor |
torch._reshape_alias(Tensor self,
LongArrayRef size,
LongArrayRef stride) |
static Tensor |
torch._reshape_from_tensor(Tensor self,
Tensor shape) |
static Tensor |
torch._s_where(Tensor condition,
Tensor self,
Tensor other) |
static Tensor |
torch._sample_dirichlet(Tensor self) |
static Tensor |
torch._sample_dirichlet(Tensor self,
GeneratorOptional generator) |
static Tensor |
torch._saturate_weight_to_fp16(Tensor weight) |
static Tensor |
torch._segment_reduce_backward(Tensor grad,
Tensor output,
Tensor data,
Pointer reduce) |
static Tensor |
torch._segment_reduce_backward(Tensor grad,
Tensor output,
Tensor data,
Pointer reduce,
TensorOptional lengths,
long axis) |
static Tensor |
torch._shape_as_tensor(Tensor self) |
static Tensor |
torch._smooth_l1_loss(Tensor input,
Tensor target) |
static Tensor |
torch._smooth_l1_loss(Tensor input,
Tensor target,
double beta) |
static Tensor |
torch._sobol_engine_ff_(Tensor self,
long n,
Tensor sobolstate,
long dimension,
long num_generated) |
static Tensor |
torch._sobol_engine_initialize_state_(Tensor self,
long dimension) |
static Tensor |
torch._sobol_engine_scramble_(Tensor self,
Tensor ltm,
long dimension) |
static Tensor |
torch._softmax_backward_data_out(Tensor grad_input,
Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._softmax_backward_data_outf(Tensor grad_output,
Tensor output,
long dim,
Tensor self,
Tensor grad_input) |
static Tensor |
torch._softmax_backward_data(Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._softmax_out(Tensor out,
Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._softmax_outf(Tensor self,
long dim,
boolean half_to_float,
Tensor out) |
static Tensor |
torch._softmax(Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._sparse_addmm(Tensor self,
Tensor sparse,
Tensor dense) |
static Tensor |
torch._sparse_addmm(Tensor self,
Tensor sparse,
Tensor dense,
Scalar beta,
Scalar alpha) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
long... size) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
long[] size,
TensorOptions options) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
LongArrayRef size) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch._sparse_coo_tensor_with_dims_and_tensors(long sparse_dim,
long dense_dim,
long[] size,
Tensor indices,
Tensor values,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_coo_tensor_with_dims_and_tensors(long sparse_dim,
long dense_dim,
long[] size,
Tensor indices,
Tensor values,
TensorOptions options) |
static Tensor |
torch._sparse_coo_tensor_with_dims_and_tensors(long sparse_dim,
long dense_dim,
LongArrayRef size,
Tensor indices,
Tensor values,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_coo_tensor_with_dims_and_tensors(long sparse_dim,
long dense_dim,
LongArrayRef size,
Tensor indices,
Tensor values,
TensorOptions options) |
static Tensor |
torch._sparse_coo_tensor_with_dims(long sparse_dim,
long dense_dim,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_coo_tensor_with_dims(long sparse_dim,
long dense_dim,
long[] size,
TensorOptions options) |
static Tensor |
torch._sparse_coo_tensor_with_dims(long sparse_dim,
long dense_dim,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_coo_tensor_with_dims(long sparse_dim,
long dense_dim,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long... size) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long[] size,
TensorOptions options) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch._sparse_log_softmax_backward_data(Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._sparse_log_softmax(Tensor self,
Dimname dim) |
static Tensor |
torch._sparse_log_softmax(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch._sparse_log_softmax(Tensor self,
long dim) |
static Tensor |
torch._sparse_log_softmax(Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._sparse_log_softmax(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch._sparse_mask_helper(Tensor t,
Tensor mask_indices) |
static Tensor |
torch._sparse_mm(Tensor sparse,
Tensor dense) |
static Tensor |
torch._sparse_softmax_backward_data(Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._sparse_softmax(Tensor self,
Dimname dim) |
static Tensor |
torch._sparse_softmax(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch._sparse_softmax(Tensor self,
long dim) |
static Tensor |
torch._sparse_softmax(Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._sparse_softmax(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch._sparse_sparse_matmul(Tensor self,
Tensor other) |
static Tensor |
torch._sparse_sum_backward(Tensor grad,
Tensor self,
long... dim) |
static Tensor |
torch._sparse_sum_backward(Tensor grad,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch._sparse_sum(Tensor self) |
static Tensor |
torch._sparse_sum(Tensor self,
long... dim) |
static Tensor |
torch._sparse_sum(Tensor self,
long[] dim,
torch.ScalarType dtype) |
static Tensor |
torch._sparse_sum(Tensor self,
LongArrayRef dim) |
static Tensor |
torch._sparse_sum(Tensor self,
LongArrayRef dim,
torch.ScalarType dtype) |
static Tensor |
torch._sparse_sum(Tensor self,
torch.ScalarType dtype) |
static Tensor |
torch._stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch._stack_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch._stack_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch._stack(TensorArrayRef tensors) |
static Tensor |
torch._stack(TensorArrayRef tensors,
long dim) |
static Tensor |
torch._standard_gamma_grad(Tensor self,
Tensor output) |
static Tensor |
torch._standard_gamma(Tensor self) |
static Tensor |
torch._standard_gamma(Tensor self,
GeneratorOptional generator) |
static Tensor |
torch._to_copy(Tensor self) |
static Tensor |
torch._to_copy(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
boolean non_blocking,
MemoryFormatOptional memory_format) |
static Tensor |
torch._to_copy(Tensor self,
TensorOptions options,
boolean non_blocking,
MemoryFormatOptional memory_format) |
static Tensor |
torch._trilinear(Tensor i1,
Tensor i2,
Tensor i3,
long[] expand1,
long[] expand2,
long[] expand3,
long... sumdim) |
static Tensor |
torch._trilinear(Tensor i1,
Tensor i2,
Tensor i3,
long[] expand1,
long[] expand2,
long[] expand3,
long[] sumdim,
long unroll_dim) |
static Tensor |
torch._trilinear(Tensor i1,
Tensor i2,
Tensor i3,
LongArrayRef expand1,
LongArrayRef expand2,
LongArrayRef expand3,
LongArrayRef sumdim) |
static Tensor |
torch._trilinear(Tensor i1,
Tensor i2,
Tensor i3,
LongArrayRef expand1,
LongArrayRef expand2,
LongArrayRef expand3,
LongArrayRef sumdim,
long unroll_dim) |
static Tensor |
torch._unsafe_view(Tensor self,
long... size) |
static Tensor |
torch._unsafe_view(Tensor self,
LongArrayRef size) |
static Tensor |
torch._weight_norm(Tensor v,
Tensor g) |
static Tensor |
torch._weight_norm(Tensor v,
Tensor g,
long dim) |
static Tensor |
torch.abs_(Tensor self) |
static Tensor |
torch.abs_out(Tensor out,
Tensor self) |
static Tensor |
torch.abs_outf(Tensor self,
Tensor out) |
static Tensor |
torch.abs(Tensor self) |
static Tensor |
torch.absolute_out(Tensor out,
Tensor self) |
static Tensor |
torch.absolute_outf(Tensor self,
Tensor out) |
static Tensor |
torch.absolute(Tensor self) |
static Tensor |
torch.acos_(Tensor self) |
static Tensor |
torch.acos_out(Tensor out,
Tensor self) |
static Tensor |
torch.acos_outf(Tensor self,
Tensor out) |
static Tensor |
torch.acos(Tensor self) |
static Tensor |
torch.acosh_(Tensor self) |
static Tensor |
torch.acosh_out(Tensor out,
Tensor self) |
static Tensor |
torch.acosh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.acosh(Tensor self) |
static Tensor |
torch.adaptive_avg_pool1d(Tensor input,
AdaptiveAvgPool1dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_avg_pool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.adaptive_avg_pool1d(Tensor self,
long... output_size) |
static Tensor |
torch.adaptive_avg_pool1d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_avg_pool1d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_avg_pool2d_out(Tensor out,
Tensor self,
long... output_size) |
static Tensor |
torch.adaptive_avg_pool2d_out(Tensor out,
Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_avg_pool2d_outf(Tensor self,
long[] output_size,
Tensor out) |
static Tensor |
torch.adaptive_avg_pool2d_outf(Tensor self,
LongArrayRef output_size,
Tensor out) |
static Tensor |
torch.adaptive_avg_pool2d(Tensor input,
AdaptiveAvgPool2dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_avg_pool2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.adaptive_avg_pool2d(Tensor self,
long... output_size) |
static Tensor |
torch.adaptive_avg_pool2d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_avg_pool2d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_avg_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self) |
static Tensor |
torch.adaptive_avg_pool3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor grad_input) |
static Tensor |
torch.adaptive_avg_pool3d_out(Tensor out,
Tensor self,
long... output_size) |
static Tensor |
torch.adaptive_avg_pool3d_out(Tensor out,
Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_avg_pool3d_outf(Tensor self,
long[] output_size,
Tensor out) |
static Tensor |
torch.adaptive_avg_pool3d_outf(Tensor self,
LongArrayRef output_size,
Tensor out) |
static Tensor |
torch.adaptive_avg_pool3d(Tensor input,
AdaptiveAvgPool3dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_avg_pool3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.adaptive_avg_pool3d(Tensor self,
long... output_size) |
static Tensor |
torch.adaptive_avg_pool3d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_avg_pool3d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_max_pool1d(Tensor input,
AdaptiveMaxPool1dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_max_pool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.adaptive_max_pool1d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_max_pool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices) |
static Tensor |
torch.adaptive_max_pool2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.adaptive_max_pool2d_backward(Tensor grad_output,
Tensor self,
Tensor indices) |
static Tensor |
torch.adaptive_max_pool2d(Tensor input,
AdaptiveMaxPool2dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_max_pool2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.adaptive_max_pool2d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_max_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices) |
static Tensor |
torch.adaptive_max_pool3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.adaptive_max_pool3d_backward(Tensor grad_output,
Tensor self,
Tensor indices) |
static Tensor |
torch.adaptive_max_pool3d(Tensor input,
AdaptiveMaxPool3dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_max_pool3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.adaptive_max_pool3d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.add_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.add_out(Tensor out,
Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.add_outf(Tensor self,
Tensor other,
Scalar alpha,
Tensor out) |
static Tensor |
torch.add(Scalar x,
Tensor y) |
static Tensor |
torch.add(Tensor self,
Scalar other) |
static Tensor |
torch.add(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch.add(Tensor self,
Tensor other) |
static Tensor |
torch.add(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.addbmm_out(Tensor out,
Tensor self,
Tensor batch1,
Tensor batch2) |
static Tensor |
torch.addbmm_out(Tensor out,
Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addbmm_outf(Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.addbmm(Tensor self,
Tensor batch1,
Tensor batch2) |
static Tensor |
torch.addbmm(Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addcdiv_out(Tensor out,
Tensor self,
Tensor tensor1,
Tensor tensor2) |
static Tensor |
torch.addcdiv_out(Tensor out,
Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value) |
static Tensor |
torch.addcdiv_outf(Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value,
Tensor out) |
static Tensor |
torch.addcdiv(Tensor self,
Tensor tensor1,
Tensor tensor2) |
static Tensor |
torch.addcdiv(Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value) |
static Tensor |
torch.addcmul_out(Tensor out,
Tensor self,
Tensor tensor1,
Tensor tensor2) |
static Tensor |
torch.addcmul_out(Tensor out,
Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value) |
static Tensor |
torch.addcmul_outf(Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value,
Tensor out) |
static Tensor |
torch.addcmul(Tensor self,
Tensor tensor1,
Tensor tensor2) |
static Tensor |
torch.addcmul(Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value) |
static Tensor |
torch.addmm_out(Tensor out,
Tensor self,
Tensor mat1,
Tensor mat2) |
static Tensor |
torch.addmm_out(Tensor out,
Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addmm_outf(Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.addmm(Tensor self,
Tensor mat1,
Tensor mat2) |
static Tensor |
torch.addmm(Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addmv_(Tensor self,
Tensor mat,
Tensor vec) |
static Tensor |
torch.addmv_(Tensor self,
Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addmv_out(Tensor out,
Tensor self,
Tensor mat,
Tensor vec) |
static Tensor |
torch.addmv_out(Tensor out,
Tensor self,
Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addmv_outf(Tensor self,
Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.addmv(Tensor self,
Tensor mat,
Tensor vec) |
static Tensor |
torch.addmv(Tensor self,
Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addr_out(Tensor out,
Tensor self,
Tensor vec1,
Tensor vec2) |
static Tensor |
torch.addr_out(Tensor out,
Tensor self,
Tensor vec1,
Tensor vec2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addr_outf(Tensor self,
Tensor vec1,
Tensor vec2,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.addr(Tensor self,
Tensor vec1,
Tensor vec2) |
static Tensor |
torch.addr(Tensor self,
Tensor vec1,
Tensor vec2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.affine_grid_generator_backward(Tensor grad,
long[] size,
boolean align_corners) |
static Tensor |
torch.affine_grid_generator_backward(Tensor grad,
LongArrayRef size,
boolean align_corners) |
static Tensor |
torch.affine_grid_generator(Tensor theta,
long[] size,
boolean align_corners) |
static Tensor |
torch.affine_grid_generator(Tensor theta,
LongArrayRef size,
boolean align_corners) |
static Tensor |
torch.affine_grid(Tensor theta,
long... size) |
static Tensor |
torch.affine_grid(Tensor theta,
long[] size,
boolean align_corners) |
static Tensor |
torch.affine_grid(Tensor theta,
LongArrayRef size) |
static Tensor |
torch.affine_grid(Tensor theta,
LongArrayRef size,
boolean align_corners) |
static Tensor |
torch.alias(Tensor self) |
static Tensor |
torch.all_out(Tensor out,
Tensor self) |
static Tensor |
torch.all_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.all_out(Tensor out,
Tensor self,
Dimname dim,
boolean keepdim) |
static Tensor |
torch.all_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.all_out(Tensor out,
Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.all_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.all_outf(Tensor self,
long dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.all_outf(Tensor self,
Tensor out) |
static Tensor |
torch.all(Tensor self) |
static Tensor |
torch.all(Tensor self,
Dimname dim) |
static Tensor |
torch.all(Tensor self,
Dimname dim,
boolean keepdim) |
static Tensor |
torch.all(Tensor self,
long dim) |
static Tensor |
torch.all(Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.alpha_dropout_(Tensor self,
double p,
boolean train) |
static Tensor |
torch.alpha_dropout(Tensor input) |
static Tensor |
torch.alpha_dropout(Tensor input,
AlphaDropoutFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.alpha_dropout
/** about the exact behavior of this functional.
|
static Tensor |
torch.alpha_dropout(Tensor input,
double p,
boolean train) |
static Tensor |
torch.alpha_dropout(Tensor input,
double p,
boolean training,
boolean inplace) |
static Tensor |
torch.amax_out(Tensor out,
Tensor self) |
static Tensor |
torch.amax_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.amax_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.amax_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.amax_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.amax(Tensor self) |
static Tensor |
torch.amax(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.amax(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.amin_out(Tensor out,
Tensor self) |
static Tensor |
torch.amin_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.amin_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.amin_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.amin_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.amin(Tensor self) |
static Tensor |
torch.amin(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.amin(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.and(Scalar x,
Tensor y) |
static Tensor |
torch.and(Tensor x,
Scalar y) |
static Tensor |
torch.and(Tensor x,
Tensor y) |
static Tensor |
torch.angle_out(Tensor out,
Tensor self) |
static Tensor |
torch.angle_outf(Tensor self,
Tensor out) |
static Tensor |
torch.angle(Tensor self) |
static Tensor |
torch.any_out(Tensor out,
Tensor self) |
static Tensor |
torch.any_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.any_out(Tensor out,
Tensor self,
Dimname dim,
boolean keepdim) |
static Tensor |
torch.any_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.any_out(Tensor out,
Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.any_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.any_outf(Tensor self,
long dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.any_outf(Tensor self,
Tensor out) |
static Tensor |
torch.any(Tensor self) |
static Tensor |
torch.any(Tensor self,
Dimname dim) |
static Tensor |
torch.any(Tensor self,
Dimname dim,
boolean keepdim) |
static Tensor |
torch.any(Tensor self,
long dim) |
static Tensor |
torch.any(Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.applySelect(Tensor self,
long dim,
long index,
long real_dim,
Device self_device,
long... self_sizes) |
static Tensor |
torch.applySelect(Tensor self,
long dim,
long index,
long real_dim,
Device self_device,
LongArrayRef self_sizes) |
static Tensor |
torch.applySlice(Tensor self,
long dim,
long start,
long stop,
long step,
boolean disable_slice_optimization,
Device self_device,
long... self_sizes) |
static Tensor |
torch.applySlice(Tensor self,
long dim,
long start,
long stop,
long step,
boolean disable_slice_optimization,
Device self_device,
LongArrayRef self_sizes) |
static Tensor |
torch.applySlicing(Tensor self,
TensorIndexArrayRef indices,
TensorVector outIndices,
boolean disable_slice_optimization,
Device self_device,
long... self_sizes) |
static Tensor |
torch.applySlicing(Tensor self,
TensorIndexArrayRef indices,
TensorVector outIndices,
boolean disable_slice_optimization,
Device self_device,
LongArrayRef self_sizes) |
static Tensor |
torch.arange_out(Tensor out,
Scalar end) |
static Tensor |
torch.arange_out(Tensor out,
Scalar start,
Scalar end) |
static Tensor |
torch.arange_out(Tensor out,
Scalar start,
Scalar end,
Scalar step) |
static Tensor |
torch.arange_outf(Scalar start,
Scalar end,
Scalar step,
Tensor out) |
static Tensor |
torch.arange_outf(Scalar end,
Tensor out) |
static Tensor |
torch.arange(Scalar end) |
static Tensor |
torch.arange(Scalar start,
Scalar end) |
static Tensor |
torch.arange(Scalar start,
Scalar end,
Scalar step) |
static Tensor |
torch.arange(Scalar start,
Scalar end,
Scalar step,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.arange(Scalar start,
Scalar end,
Scalar step,
TensorOptions options) |
static Tensor |
torch.arange(Scalar start,
Scalar end,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.arange(Scalar start,
Scalar end,
TensorOptions options) |
static Tensor |
torch.arange(Scalar end,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.arange(Scalar end,
TensorOptions options) |
static Tensor |
torch.arccos_(Tensor self) |
static Tensor |
torch.arccos_out(Tensor out,
Tensor self) |
static Tensor |
torch.arccos_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arccos(Tensor self) |
static Tensor |
torch.arccosh_(Tensor self) |
static Tensor |
torch.arccosh_out(Tensor out,
Tensor self) |
static Tensor |
torch.arccosh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arccosh(Tensor self) |
static Tensor |
torch.arcsin_(Tensor self) |
static Tensor |
torch.arcsin_out(Tensor out,
Tensor self) |
static Tensor |
torch.arcsin_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arcsin(Tensor self) |
static Tensor |
torch.arcsinh_(Tensor self) |
static Tensor |
torch.arcsinh_out(Tensor out,
Tensor self) |
static Tensor |
torch.arcsinh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arcsinh(Tensor self) |
static Tensor |
torch.arctan_(Tensor self) |
static Tensor |
torch.arctan_out(Tensor out,
Tensor self) |
static Tensor |
torch.arctan_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arctan(Tensor self) |
static Tensor |
torch.arctanh_(Tensor self) |
static Tensor |
torch.arctanh_out(Tensor out,
Tensor self) |
static Tensor |
torch.arctanh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arctanh(Tensor self) |
static Tensor |
torch.argmax_out(Tensor out,
Tensor self) |
static Tensor |
torch.argmax_out(Tensor out,
Tensor self,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.argmax_outf(Tensor self,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.argmax(Tensor self) |
static Tensor |
torch.argmax(Tensor self,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.argmin_out(Tensor out,
Tensor self) |
static Tensor |
torch.argmin_out(Tensor out,
Tensor self,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.argmin_outf(Tensor self,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.argmin(Tensor self) |
static Tensor |
torch.argmin(Tensor self,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.argsort(Tensor self) |
static Tensor |
torch.argsort(Tensor self,
Dimname dim) |
static Tensor |
torch.argsort(Tensor self,
Dimname dim,
boolean descending) |
static Tensor |
torch.argsort(Tensor self,
long dim,
boolean descending) |
static Tensor |
torch.as_strided_(Tensor self,
long[] size,
long... stride) |
static Tensor |
torch.as_strided_(Tensor self,
long[] size,
long[] stride,
LongOptional storage_offset) |
static Tensor |
torch.as_strided_(Tensor self,
LongArrayRef size,
LongArrayRef stride) |
static Tensor |
torch.as_strided_(Tensor self,
LongArrayRef size,
LongArrayRef stride,
LongOptional storage_offset) |
static Tensor |
torch.as_strided(Tensor self,
long[] size,
long... stride) |
static Tensor |
torch.as_strided(Tensor self,
long[] size,
long[] stride,
LongOptional storage_offset) |
static Tensor |
torch.as_strided(Tensor self,
LongArrayRef size,
LongArrayRef stride) |
static Tensor |
torch.as_strided(Tensor self,
LongArrayRef size,
LongArrayRef stride,
LongOptional storage_offset) |
static Tensor |
torch.asin_(Tensor self) |
static Tensor |
torch.asin_out(Tensor out,
Tensor self) |
static Tensor |
torch.asin_outf(Tensor self,
Tensor out) |
static Tensor |
torch.asin(Tensor self) |
static Tensor |
torch.asinh_(Tensor self) |
static Tensor |
torch.asinh_out(Tensor out,
Tensor self) |
static Tensor |
torch.asinh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.asinh(Tensor self) |
static Tensor |
torch.atan_(Tensor self) |
static Tensor |
torch.atan_out(Tensor out,
Tensor self) |
static Tensor |
torch.atan_outf(Tensor self,
Tensor out) |
static Tensor |
torch.atan(Tensor self) |
static Tensor |
torch.atan2_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.atan2_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.atan2(Tensor self,
Tensor other) |
static Tensor |
torch.atanh_(Tensor self) |
static Tensor |
torch.atanh_out(Tensor out,
Tensor self) |
static Tensor |
torch.atanh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.atanh(Tensor self) |
static Tensor |
torch.atleast_1d(Tensor self) |
static Tensor |
torch.atleast_2d(Tensor self) |
static Tensor |
torch.atleast_3d(Tensor self) |
static Tensor |
torch.avg_pool1d(Tensor input,
AvgPool1dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.avg_pool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.avg_pool1d(Tensor self,
long... kernel_size) |
static Tensor |
torch.avg_pool1d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad) |
static Tensor |
torch.avg_pool1d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.avg_pool1d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad) |
static Tensor |
torch.avg_pool1d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
boolean ceil_mode,
boolean count_include_pad) |
static Tensor |
torch.avg_pool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor grad_input) |
static Tensor |
torch.avg_pool2d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor grad_input) |
static Tensor |
torch.avg_pool2d_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_out(Tensor out,
Tensor self,
long... kernel_size) |
static Tensor |
torch.avg_pool2d_out(Tensor out,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_out(Tensor out,
Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.avg_pool2d_out(Tensor out,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_outf(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor out) |
static Tensor |
torch.avg_pool2d_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor out) |
static Tensor |
torch.avg_pool2d(Tensor input,
AvgPool2dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.avg_pool2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.avg_pool2d(Tensor self,
long... kernel_size) |
static Tensor |
torch.avg_pool2d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.avg_pool2d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor grad_input) |
static Tensor |
torch.avg_pool3d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor grad_input) |
static Tensor |
torch.avg_pool3d_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_out(Tensor out,
Tensor self,
long... kernel_size) |
static Tensor |
torch.avg_pool3d_out(Tensor out,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_out(Tensor out,
Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.avg_pool3d_out(Tensor out,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_outf(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor out) |
static Tensor |
torch.avg_pool3d_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor out) |
static Tensor |
torch.avg_pool3d(Tensor input,
AvgPool3dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.avg_pool3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.avg_pool3d(Tensor self,
long... kernel_size) |
static Tensor |
torch.avg_pool3d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.avg_pool3d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.baddbmm_out(Tensor out,
Tensor self,
Tensor batch1,
Tensor batch2) |
static Tensor |
torch.baddbmm_out(Tensor out,
Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.baddbmm_outf(Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.baddbmm(Tensor self,
Tensor batch1,
Tensor batch2) |
static Tensor |
torch.baddbmm(Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.bartlett_window(long window_length) |
static Tensor |
torch.bartlett_window(long window_length,
boolean periodic) |
static Tensor |
torch.bartlett_window(long window_length,
boolean periodic,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.bartlett_window(long window_length,
boolean periodic,
TensorOptions options) |
static Tensor |
torch.bartlett_window(long window_length,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.bartlett_window(long window_length,
TensorOptions options) |
static Tensor |
torch.batch_norm_backward_elemt(Tensor grad_out,
Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional weight,
Tensor mean_dy,
Tensor mean_dy_xmu,
Tensor count) |
static Tensor |
torch.batch_norm_elemt_out(Tensor out,
Tensor input,
TensorOptional weight,
TensorOptional bias,
Tensor mean,
Tensor invstd,
double eps) |
static Tensor |
torch.batch_norm_elemt_outf(Tensor input,
TensorOptional weight,
TensorOptional bias,
Tensor mean,
Tensor invstd,
double eps,
Tensor out) |
static Tensor |
torch.batch_norm_elemt(Tensor input,
TensorOptional weight,
TensorOptional bias,
Tensor mean,
Tensor invstd,
double eps) |
static Tensor |
torch.batch_norm(Tensor input,
TensorOptional weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean training,
double momentum,
double eps,
boolean cudnn_enabled) |
static Tensor |
torch.batch_norm(Tensor input,
Tensor running_mean,
Tensor running_var) |
static Tensor |
torch.batch_norm(Tensor input,
Tensor running_mean,
Tensor running_var,
BatchNormFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.batch_norm
/** about the exact behavior of this functional.
|
static Tensor |
torch.batch_norm(Tensor input,
Tensor running_mean,
Tensor running_var,
Tensor weight,
Tensor bias,
boolean training,
DoubleOptional momentum,
double eps) |
static Tensor |
torch.bernoulli_out(Tensor out,
Tensor self) |
static Tensor |
torch.bernoulli_out(Tensor out,
Tensor self,
GeneratorOptional generator) |
static Tensor |
torch.bernoulli_outf(Tensor self,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.bernoulli(Tensor self) |
static Tensor |
torch.bernoulli(Tensor self,
double p) |
static Tensor |
torch.bernoulli(Tensor self,
double p,
GeneratorOptional generator) |
static Tensor |
torch.bernoulli(Tensor self,
GeneratorOptional generator) |
static Tensor |
torch.bilinear(Tensor input1,
Tensor input2,
Tensor weight) |
static Tensor |
torch.bilinear(Tensor input1,
Tensor input2,
Tensor weight,
Tensor bias) |
static Tensor |
torch.bilinear(Tensor input1,
Tensor input2,
Tensor weight,
TensorOptional bias) |
static Tensor |
torch.binary_cross_entropy_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction) |
static Tensor |
torch.binary_cross_entropy_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
Tensor grad_input) |
static Tensor |
torch.binary_cross_entropy_backward(Tensor grad_output,
Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy_backward(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction) |
static Tensor |
torch.binary_cross_entropy_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy_out(Tensor out,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction) |
static Tensor |
torch.binary_cross_entropy_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
Tensor out) |
static Tensor |
torch.binary_cross_entropy_with_logits_backward(Tensor grad_output,
Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy_with_logits_backward(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
TensorOptional pos_weight,
long reduction) |
static Tensor |
torch.binary_cross_entropy_with_logits(Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy_with_logits(Tensor input,
Tensor target,
BCEWithLogitsLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.binary_cross_entropy_with_logits
/** about the exact behavior of this functional.
|
static Tensor |
torch.binary_cross_entropy_with_logits(Tensor input,
Tensor target,
Tensor weight,
loss_reduction_t reduction,
Tensor pos_weight) |
static Tensor |
torch.binary_cross_entropy_with_logits(Tensor self,
Tensor target,
TensorOptional weight,
TensorOptional pos_weight,
long reduction) |
static Tensor |
torch.binary_cross_entropy(Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy(Tensor input,
Tensor target,
BCELossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.binary_cross_entropy
/** about the exact behavior of this functional.
|
static Tensor |
torch.binary_cross_entropy(Tensor input,
Tensor target,
Tensor weight,
loss_reduction_t reduction) |
static Tensor |
torch.binary_cross_entropy(Tensor self,
Tensor target,
TensorOptional weight,
long reduction) |
static Tensor |
torch.bincount(Tensor self) |
static Tensor |
torch.bincount(Tensor self,
TensorOptional weights,
long minlength) |
static Tensor |
torch.binomial(Tensor count,
Tensor prob) |
static Tensor |
torch.binomial(Tensor count,
Tensor prob,
GeneratorOptional generator) |
static Tensor |
torch.bitwise_and_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_and_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_and_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.bitwise_and_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.bitwise_and(Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_and(Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_left_shift_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_left_shift_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_left_shift_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.bitwise_left_shift_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.bitwise_left_shift(Scalar self,
Tensor other) |
static Tensor |
torch.bitwise_left_shift(Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_left_shift(Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_not_out(Tensor out,
Tensor self) |
static Tensor |
torch.bitwise_not_outf(Tensor self,
Tensor out) |
static Tensor |
torch.bitwise_not(Tensor self) |
static Tensor |
torch.bitwise_or_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_or_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_or_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.bitwise_or_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.bitwise_or(Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_or(Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_right_shift_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_right_shift_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_right_shift_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.bitwise_right_shift_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.bitwise_right_shift(Scalar self,
Tensor other) |
static Tensor |
torch.bitwise_right_shift(Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_right_shift(Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_xor_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_xor_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_xor_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.bitwise_xor_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.bitwise_xor(Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_xor(Tensor self,
Tensor other) |
static Tensor |
torch.blackman_window(long window_length) |
static Tensor |
torch.blackman_window(long window_length,
boolean periodic) |
static Tensor |
torch.blackman_window(long window_length,
boolean periodic,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.blackman_window(long window_length,
boolean periodic,
TensorOptions options) |
static Tensor |
torch.blackman_window(long window_length,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.blackman_window(long window_length,
TensorOptions options) |
static Tensor |
torch.block_diag(TensorArrayRef tensors) |
static Tensor |
torch.bmm_out(Tensor out,
Tensor self,
Tensor mat2) |
static Tensor |
torch.bmm_outf(Tensor self,
Tensor mat2,
Tensor out) |
static Tensor |
torch.bmm(Tensor self,
Tensor mat2) |
static Tensor |
torch.boolToIndexingTensor(Tensor self,
boolean value,
Device self_device) |
static Tensor |
torch.boolToIndexingTensorCPUOrCUDA(Tensor self,
boolean value) |
static Tensor |
torch.boolToIndexingTensorNonNativeDeviceType(Tensor self,
boolean value) |
static Tensor |
torch.broadcast_to(Tensor self,
long... size) |
static Tensor |
torch.broadcast_to(Tensor self,
LongArrayRef size) |
static Tensor |
torch.bucketize_out(Tensor out,
Tensor self,
Tensor boundaries) |
static Tensor |
torch.bucketize_out(Tensor out,
Tensor self,
Tensor boundaries,
boolean out_int32,
boolean right) |
static Tensor |
torch.bucketize_outf(Tensor self,
Tensor boundaries,
boolean out_int32,
boolean right,
Tensor out) |
static Tensor |
torch.bucketize(Scalar self,
Tensor boundaries) |
static Tensor |
torch.bucketize(Scalar self,
Tensor boundaries,
boolean out_int32,
boolean right) |
static Tensor |
torch.bucketize(Tensor self,
Tensor boundaries) |
static Tensor |
torch.bucketize(Tensor self,
Tensor boundaries,
boolean out_int32,
boolean right) |
static Tensor |
torch.cartesian_prod(TensorArrayRef tensors) |
static Tensor |
torch.cat_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.cat_out(Tensor out,
TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.cat_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch.cat_outf(TensorArrayRef tensors,
Dimname dim,
Tensor out) |
static Tensor |
torch.cat_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch.cat(TensorArrayRef tensors) |
static Tensor |
torch.cat(TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.cat(TensorArrayRef tensors,
long dim) |
static Tensor |
torch.cdist(Tensor x1,
Tensor x2) |
static Tensor |
torch.cdist(Tensor x1,
Tensor x2,
double p,
LongOptional compute_mode) |
static Tensor |
torch.ceil_(Tensor self) |
static Tensor |
torch.ceil_out(Tensor out,
Tensor self) |
static Tensor |
torch.ceil_outf(Tensor self,
Tensor out) |
static Tensor |
torch.ceil(Tensor self) |
static Tensor |
torch.celu_(Tensor self) |
static Tensor |
torch.celu_(Tensor self,
Scalar alpha) |
static Tensor |
torch.celu(Tensor self) |
static Tensor |
torch.celu(Tensor input,
CELUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.celu
/** about the exact behavior of this functional.
|
static Tensor |
torch.celu(Tensor input,
double alpha,
boolean inplace) |
static Tensor |
torch.celu(Tensor self,
Scalar alpha) |
static Tensor |
torch.chain_matmul_out(Tensor out,
TensorArrayRef matrices) |
static Tensor |
torch.chain_matmul_outf(TensorArrayRef matrices,
Tensor out) |
static Tensor |
torch.chain_matmul(TensorArrayRef matrices) |
static Tensor |
torch.channel_shuffle(Tensor self,
long groups) |
static Tensor |
torch.cholesky_inverse_out(Tensor out,
Tensor self) |
static Tensor |
torch.cholesky_inverse_out(Tensor out,
Tensor self,
boolean upper) |
static Tensor |
torch.cholesky_inverse_outf(Tensor self,
boolean upper,
Tensor out) |
static Tensor |
torch.cholesky_inverse(Tensor self) |
static Tensor |
torch.cholesky_inverse(Tensor self,
boolean upper) |
static Tensor |
torch.cholesky_out(Tensor out,
Tensor self) |
static Tensor |
torch.cholesky_out(Tensor out,
Tensor self,
boolean upper) |
static Tensor |
torch.cholesky_outf(Tensor self,
boolean upper,
Tensor out) |
static Tensor |
torch.cholesky_solve_out(Tensor out,
Tensor self,
Tensor input2) |
static Tensor |
torch.cholesky_solve_out(Tensor out,
Tensor self,
Tensor input2,
boolean upper) |
static Tensor |
torch.cholesky_solve_outf(Tensor self,
Tensor input2,
boolean upper,
Tensor out) |
static Tensor |
torch.cholesky_solve(Tensor self,
Tensor input2) |
static Tensor |
torch.cholesky_solve(Tensor self,
Tensor input2,
boolean upper) |
static Tensor |
torch.cholesky(Tensor self) |
static Tensor |
torch.cholesky(Tensor self,
boolean upper) |
static Tensor |
torch.clamp_(Tensor self) |
static Tensor |
torch.clamp_(Tensor self,
ScalarOptional min) |
static Tensor |
torch.clamp_(Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clamp_(Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clamp_max_(Tensor self,
Scalar max) |
static Tensor |
torch.clamp_max_(Tensor self,
Tensor max) |
static Tensor |
torch.clamp_max_out(Tensor out,
Tensor self,
Scalar max) |
static Tensor |
torch.clamp_max_out(Tensor out,
Tensor self,
Tensor max) |
static Tensor |
torch.clamp_max_outf(Tensor self,
Scalar max,
Tensor out) |
static Tensor |
torch.clamp_max_outf(Tensor self,
Tensor max,
Tensor out) |
static Tensor |
torch.clamp_max(Tensor self,
Scalar max) |
static Tensor |
torch.clamp_max(Tensor self,
Tensor max) |
static Tensor |
torch.clamp_min_(Tensor self,
Scalar min) |
static Tensor |
torch.clamp_min_(Tensor self,
Tensor min) |
static Tensor |
torch.clamp_min_out(Tensor out,
Tensor self,
Scalar min) |
static Tensor |
torch.clamp_min_out(Tensor out,
Tensor self,
Tensor min) |
static Tensor |
torch.clamp_min_outf(Tensor self,
Scalar min,
Tensor out) |
static Tensor |
torch.clamp_min_outf(Tensor self,
Tensor min,
Tensor out) |
static Tensor |
torch.clamp_min(Tensor self,
Scalar min) |
static Tensor |
torch.clamp_min(Tensor self,
Tensor min) |
static Tensor |
torch.clamp_out(Tensor out,
Tensor self) |
static Tensor |
torch.clamp_out(Tensor out,
Tensor self,
ScalarOptional min) |
static Tensor |
torch.clamp_out(Tensor out,
Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clamp_out(Tensor out,
Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clamp_outf(Tensor self,
ScalarOptional min,
ScalarOptional max,
Tensor out) |
static Tensor |
torch.clamp_outf(Tensor self,
TensorOptional min,
TensorOptional max,
Tensor out) |
static Tensor |
torch.clamp(Tensor self) |
static Tensor |
torch.clamp(Tensor self,
ScalarOptional min) |
static Tensor |
torch.clamp(Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clamp(Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clip_(Tensor self) |
static Tensor |
torch.clip_(Tensor self,
ScalarOptional min) |
static Tensor |
torch.clip_(Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clip_(Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clip_out(Tensor out,
Tensor self) |
static Tensor |
torch.clip_out(Tensor out,
Tensor self,
ScalarOptional min) |
static Tensor |
torch.clip_out(Tensor out,
Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clip_out(Tensor out,
Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clip_outf(Tensor self,
ScalarOptional min,
ScalarOptional max,
Tensor out) |
static Tensor |
torch.clip_outf(Tensor self,
TensorOptional min,
TensorOptional max,
Tensor out) |
static Tensor |
torch.clip(Tensor self) |
static Tensor |
torch.clip(Tensor self,
ScalarOptional min) |
static Tensor |
torch.clip(Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clip(Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clone(Tensor self) |
static Tensor |
torch.clone(Tensor self,
MemoryFormatOptional memory_format) |
static Tensor |
torch.col2im_backward_out(Tensor grad_input,
Tensor grad_output,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.col2im_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.col2im_backward_outf(Tensor grad_output,
long[] kernel_size,
long[] dilation,
long[] padding,
long[] stride,
Tensor grad_input) |
static Tensor |
torch.col2im_backward_outf(Tensor grad_output,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride,
Tensor grad_input) |
static Tensor |
torch.col2im_backward(Tensor grad_output,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.col2im_backward(Tensor grad_output,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.col2im_out(Tensor out,
Tensor self,
long[] output_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.col2im_out(Tensor out,
Tensor self,
LongArrayRef output_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.col2im_outf(Tensor self,
long[] output_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long[] stride,
Tensor out) |
static Tensor |
torch.col2im_outf(Tensor self,
LongArrayRef output_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride,
Tensor out) |
static Tensor |
torch.col2im(Tensor self,
long[] output_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.col2im(Tensor self,
LongArrayRef output_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.column_stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.column_stack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.column_stack(TensorArrayRef tensors) |
static Tensor |
torch.combinations(Tensor self) |
static Tensor |
torch.combinations(Tensor self,
long r,
boolean with_replacement) |
static Tensor |
torch.complex_out(Tensor out,
Tensor real,
Tensor imag) |
static Tensor |
torch.complex_outf(Tensor real,
Tensor imag,
Tensor out) |
static Tensor |
torch.concat_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.concat_out(Tensor out,
TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.concat_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch.concat_outf(TensorArrayRef tensors,
Dimname dim,
Tensor out) |
static Tensor |
torch.concat_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch.concat(TensorArrayRef tensors) |
static Tensor |
torch.concat(TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.concat(TensorArrayRef tensors,
long dim) |
static Tensor |
torch.conj_physical_(Tensor self) |
static Tensor |
torch.conj_physical_out(Tensor out,
Tensor self) |
static Tensor |
torch.conj_physical_outf(Tensor self,
Tensor out) |
static Tensor |
torch.conj_physical(Tensor self) |
static Tensor |
torch.conj(Tensor tensor) |
static Tensor |
torch.constant_(Tensor tensor,
Scalar value)
Fills the given
tensor with the provided value in-place, and returns it. |
static Tensor |
torch.constant_pad_nd(Tensor self,
long... pad) |
static Tensor |
torch.constant_pad_nd(Tensor self,
long[] pad,
Scalar value) |
static Tensor |
torch.constant_pad_nd(Tensor self,
LongArrayRef pad) |
static Tensor |
torch.constant_pad_nd(Tensor self,
LongArrayRef pad,
Scalar value) |
static Tensor |
torch.conv_depthwise3d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long... dilation) |
static Tensor |
torch.conv_depthwise3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static Tensor |
torch.conv_tbc(Tensor self,
Tensor weight,
Tensor bias) |
static Tensor |
torch.conv_tbc(Tensor self,
Tensor weight,
Tensor bias,
long pad) |
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight,
ConvTranspose1dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv_transpose1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight,
Tensor bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight,
Tensor bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight,
ConvTranspose2dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv_transpose2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight,
Tensor bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight,
Tensor bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight,
ConvTranspose3dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv_transpose3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight,
Tensor bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight,
Tensor bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
Conv1dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
Tensor bias,
LongPointer stride,
conv_padding_t1 padding,
LongPointer dilation,
long groups) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
Conv2dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
Tensor bias,
LongPointer stride,
conv_padding_t2 padding,
LongPointer dilation,
long groups) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
Conv3dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
Tensor bias,
LongPointer stride,
conv_padding_t3 padding,
LongPointer dilation,
long groups) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.convolution_overrideable(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long[] output_padding,
long groups) |
static Tensor |
torch.convolution_overrideable(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding,
long groups) |
static Tensor |
torch.convolution(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long[] output_padding,
long groups) |
static Tensor |
torch.convolution(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding,
long groups) |
static Tensor |
torch.copy_sparse_to_sparse_(Tensor self,
Tensor src) |
static Tensor |
torch.copy_sparse_to_sparse_(Tensor self,
Tensor src,
boolean non_blocking) |
static Tensor |
torch.copysign_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.copysign_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.copysign_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.copysign_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.copysign(Tensor self,
Scalar other) |
static Tensor |
torch.copysign(Tensor self,
Tensor other) |
static Tensor |
torch.corrcoef(Tensor self) |
static Tensor |
torch.cos_(Tensor self) |
static Tensor |
torch.cos_out(Tensor out,
Tensor self) |
static Tensor |
torch.cos_outf(Tensor self,
Tensor out) |
static Tensor |
torch.cos(Tensor self) |
static Tensor |
torch.cosh_(Tensor self) |
static Tensor |
torch.cosh_out(Tensor out,
Tensor self) |
static Tensor |
torch.cosh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.cosh(Tensor self) |
static Tensor |
torch.cosine_embedding_loss(Tensor input1,
Tensor input2,
Tensor target) |
static Tensor |
torch.cosine_embedding_loss(Tensor input1,
Tensor input2,
Tensor target,
CosineEmbeddingLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.cosine_embedding_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.cosine_embedding_loss(Tensor input1,
Tensor input2,
Tensor target,
double margin,
long reduction) |
static Tensor |
torch.cosine_embedding_loss(Tensor input1,
Tensor input2,
Tensor target,
double margin,
loss_reduction_t reduction) |
static Tensor |
torch.cosine_similarity(Tensor x1,
Tensor x2) |
static Tensor |
torch.cosine_similarity(Tensor x1,
Tensor x2,
CosineSimilarityOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.cosine_similarity
/** about the exact behavior of this functional.
|
static Tensor |
torch.cosine_similarity(Tensor x1,
Tensor x2,
long dim,
double eps) |
static Tensor |
torch.count_nonzero(Tensor self) |
static Tensor |
torch.count_nonzero(Tensor self,
long... dim) |
static Tensor |
torch.count_nonzero(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.count_nonzero(Tensor self,
LongOptional dim) |
static Tensor |
torch.cov(Tensor self) |
static Tensor |
torch.cov(Tensor self,
long correction,
TensorOptional fweights,
TensorOptional aweights) |
static Tensor |
torch.cross_entropy_loss(Tensor self,
Tensor target) |
static Tensor |
torch.cross_entropy_loss(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
double label_smoothing) |
static Tensor |
torch.cross_entropy(Tensor input,
Tensor target) |
static Tensor |
torch.cross_entropy(Tensor input,
Tensor target,
CrossEntropyLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.cross_entropy
/** about the exact behavior of this functional.
|
static Tensor |
torch.cross_entropy(Tensor input,
Tensor target,
Tensor weight,
long ignore_index,
loss_reduction_t reduction,
double label_smoothing) |
static Tensor |
torch.cross_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.cross_out(Tensor out,
Tensor self,
Tensor other,
LongOptional dim) |
static Tensor |
torch.cross_outf(Tensor self,
Tensor other,
LongOptional dim,
Tensor out) |
static Tensor |
torch.cross(Tensor self,
Tensor other) |
static Tensor |
torch.cross(Tensor self,
Tensor other,
LongOptional dim) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
long[] input_lengths,
long... target_lengths) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
long[] input_lengths,
long[] target_lengths,
long blank,
long reduction,
boolean zero_infinity) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths,
long blank,
long reduction,
boolean zero_infinity) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
Tensor input_lengths,
Tensor target_lengths) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
Tensor input_lengths,
Tensor target_lengths,
CTCLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.ctc_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
Tensor input_lengths,
Tensor target_lengths,
long blank,
long reduction,
boolean zero_infinity) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
Tensor input_lengths,
Tensor target_lengths,
long blank,
loss_reduction_t reduction,
boolean zero_infinity) |
static Tensor |
torch.cudnn_affine_grid_generator_backward(Tensor grad,
long N,
long C,
long H,
long W) |
static Tensor |
torch.cudnn_affine_grid_generator(Tensor theta,
long N,
long C,
long H,
long W) |
static Tensor |
torch.cudnn_convolution_add_relu(Tensor self,
Tensor weight,
Tensor z,
ScalarOptional alpha,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
long groups) |
static Tensor |
torch.cudnn_convolution_add_relu(Tensor self,
Tensor weight,
Tensor z,
ScalarOptional alpha,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.cudnn_convolution_backward_input(long[] self_size,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_backward_input(LongArrayRef self_size,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_backward_weight(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_backward_weight(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_relu(Tensor self,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
long groups) |
static Tensor |
torch.cudnn_convolution_relu(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.cudnn_convolution_transpose_backward_input(Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose_backward_input(Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose_backward_weight(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose_backward_weight(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_grid_sampler(Tensor self,
Tensor grid) |
static Tensor |
torch.cummaxmin_backward(Tensor grad,
Tensor input,
Tensor indices,
long dim) |
static Tensor |
torch.cumprod_backward(Tensor grad,
Tensor input,
long dim,
Tensor output) |
static Tensor |
torch.cumprod_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.cumprod_out(Tensor out,
Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumprod_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.cumprod_out(Tensor out,
Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumprod_outf(Tensor self,
Dimname dim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.cumprod_outf(Tensor self,
long dim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.cumprod(Tensor self,
Dimname dim) |
static Tensor |
torch.cumprod(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumprod(Tensor self,
long dim) |
static Tensor |
torch.cumprod(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumsum_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.cumsum_out(Tensor out,
Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumsum_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.cumsum_out(Tensor out,
Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumsum_outf(Tensor self,
Dimname dim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.cumsum_outf(Tensor self,
long dim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.cumsum(Tensor self,
Dimname dim) |
static Tensor |
torch.cumsum(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumsum(Tensor self,
long dim) |
static Tensor |
torch.cumsum(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumulative_trapezoid(Tensor y) |
static Tensor |
torch.cumulative_trapezoid(Tensor y,
Scalar dx,
long dim) |
static Tensor |
torch.cumulative_trapezoid(Tensor y,
Tensor x) |
static Tensor |
torch.cumulative_trapezoid(Tensor y,
Tensor x,
long dim) |
static Tensor |
torch.deg2rad_(Tensor self) |
static Tensor |
torch.deg2rad_out(Tensor out,
Tensor self) |
static Tensor |
torch.deg2rad_outf(Tensor self,
Tensor out) |
static Tensor |
torch.deg2rad(Tensor self) |
static Tensor |
torch.dequantize(Tensor self) |
static Tensor |
torch.det(Tensor self) |
static Tensor |
torch.detach_(Tensor self) |
static Tensor |
torch.detach(Tensor self) |
static Tensor |
torch.diag_backward(Tensor grad,
long[] input_sizes,
long diagonal) |
static Tensor |
torch.diag_backward(Tensor grad,
LongArrayRef input_sizes,
long diagonal) |
static Tensor |
torch.diag_embed(Tensor self) |
static Tensor |
torch.diag_embed(Tensor self,
long offset,
long dim1,
long dim2) |
static Tensor |
torch.diag_out(Tensor out,
Tensor self) |
static Tensor |
torch.diag_out(Tensor out,
Tensor self,
long diagonal) |
static Tensor |
torch.diag_outf(Tensor self,
long diagonal,
Tensor out) |
static Tensor |
torch.diag(Tensor self) |
static Tensor |
torch.diag(Tensor self,
long diagonal) |
static Tensor |
torch.diagflat(Tensor self) |
static Tensor |
torch.diagflat(Tensor self,
long offset) |
static Tensor |
torch.diagonal_backward(Tensor grad_output,
long[] input_sizes,
long offset,
long dim1,
long dim2) |
static Tensor |
torch.diagonal_backward(Tensor grad_output,
LongArrayRef input_sizes,
long offset,
long dim1,
long dim2) |
static Tensor |
torch.diagonal(Tensor self) |
static Tensor |
torch.diagonal(Tensor self,
Dimname outdim,
Dimname dim1,
Dimname dim2) |
static Tensor |
torch.diagonal(Tensor self,
Dimname outdim,
Dimname dim1,
Dimname dim2,
long offset) |
static Tensor |
torch.diagonal(Tensor self,
long offset,
long dim1,
long dim2) |
static Tensor |
torch.diff_out(Tensor out,
Tensor self) |
static Tensor |
torch.diff_out(Tensor out,
Tensor self,
long n,
long dim,
TensorOptional prepend,
TensorOptional append) |
static Tensor |
torch.diff_outf(Tensor self,
long n,
long dim,
TensorOptional prepend,
TensorOptional append,
Tensor out) |
static Tensor |
torch.diff(Tensor self) |
static Tensor |
torch.diff(Tensor self,
long n,
long dim,
TensorOptional prepend,
TensorOptional append) |
static Tensor |
torch.digamma_out(Tensor out,
Tensor self) |
static Tensor |
torch.digamma_outf(Tensor self,
Tensor out) |
static Tensor |
torch.digamma(Tensor self) |
static Tensor |
torch.dirac_(Tensor tensor)
Fills the given
tensor with the Dirac delta function in-place, and returns
it. |
static Tensor |
torch.dispatch_index_put_(Tensor self,
TensorVector indices,
Tensor value) |
static Tensor |
torch.dispatch_index(Tensor self,
TensorVector indices) |
static Tensor |
torch.dist(Tensor self,
Tensor other) |
static Tensor |
torch.dist(Tensor self,
Tensor other,
Scalar p) |
static Tensor |
torch.div_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.div_out(Tensor out,
Tensor self,
Tensor other,
Pointer rounding_mode) |
static Tensor |
torch.div_outf(Tensor self,
Tensor other,
Pointer rounding_mode,
Tensor out) |
static Tensor |
torch.div_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.div(Tensor self,
Scalar other) |
static Tensor |
torch.div(Tensor self,
Scalar other,
Pointer rounding_mode) |
static Tensor |
torch.div(Tensor self,
Tensor other) |
static Tensor |
torch.div(Tensor self,
Tensor other,
Pointer rounding_mode) |
static Tensor |
torch.divide_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.divide_out(Tensor out,
Tensor self,
Tensor other,
Pointer rounding_mode) |
static Tensor |
torch.divide_outf(Tensor self,
Tensor other,
Pointer rounding_mode,
Tensor out) |
static Tensor |
torch.divide_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.divide(Scalar x,
Tensor y) |
static Tensor |
torch.divide(Tensor self,
Scalar other) |
static Tensor |
torch.divide(Tensor self,
Scalar other,
Pointer rounding_mode) |
static Tensor |
torch.divide(Tensor self,
Tensor other) |
static Tensor |
torch.divide(Tensor self,
Tensor other,
Pointer rounding_mode) |
static Tensor |
torch.dot_out(Tensor out,
Tensor self,
Tensor tensor) |
static Tensor |
torch.dot_outf(Tensor self,
Tensor tensor,
Tensor out) |
static Tensor |
torch.dot(Tensor self,
Tensor tensor) |
static Tensor |
torch.dropout_(Tensor self,
double p,
boolean train) |
static Tensor |
torch.dropout(Tensor input) |
static Tensor |
torch.dropout(Tensor input,
double p,
boolean train) |
static Tensor |
torch.dropout(Tensor input,
double p,
boolean training,
boolean inplace)
Computes the p-norm distance between every pair of row vectors in the input.
|
static Tensor |
torch.dropout(Tensor input,
DropoutFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.dropout
/** about the exact behavior of this functional.
|
static Tensor |
torch.dropout2d(Tensor input) |
static Tensor |
torch.dropout2d(Tensor input,
double p,
boolean training,
boolean inplace) |
static Tensor |
torch.dropout2d(Tensor input,
DropoutFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.dropout2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.dropout3d(Tensor input) |
static Tensor |
torch.dropout3d(Tensor input,
double p,
boolean training,
boolean inplace) |
static Tensor |
torch.dropout3d(Tensor input,
DropoutFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.dropout3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.dstack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.dstack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.dstack(TensorArrayRef tensors) |
static Tensor |
torch.einsum(Pointer equation,
TensorArrayRef tensors) |
static Tensor |
torch.elu_(Tensor self) |
static Tensor |
torch.elu_(Tensor self,
Scalar alpha,
Scalar scale,
Scalar input_scale) |
static Tensor |
torch.elu_backward_out(Tensor grad_input,
Tensor grad_output,
Scalar alpha,
Scalar scale,
Scalar input_scale,
boolean is_result,
Tensor self_or_result) |
static Tensor |
torch.elu_backward_outf(Tensor grad_output,
Scalar alpha,
Scalar scale,
Scalar input_scale,
boolean is_result,
Tensor self_or_result,
Tensor grad_input) |
static Tensor |
torch.elu_backward(Tensor grad_output,
Scalar alpha,
Scalar scale,
Scalar input_scale,
boolean is_result,
Tensor self_or_result) |
static Tensor |
torch.elu_out(Tensor out,
Tensor self) |
static Tensor |
torch.elu_out(Tensor out,
Tensor self,
Scalar alpha,
Scalar scale,
Scalar input_scale) |
static Tensor |
torch.elu_outf(Tensor self,
Scalar alpha,
Scalar scale,
Scalar input_scale,
Tensor out) |
static Tensor |
torch.elu(Tensor self) |
static Tensor |
torch.elu(Tensor input,
double alpha,
boolean inplace)
Options for the
InstanceNorm3d module. |
static Tensor |
torch.elu(Tensor input,
ELUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.elu
/** about the exact behavior of this functional.
|
static Tensor |
torch.elu(Tensor self,
Scalar alpha,
Scalar scale,
Scalar input_scale) |
static Tensor |
torch.embedding_backward(Tensor grad,
Tensor indices,
long num_weights,
long padding_idx,
boolean scale_grad_by_freq,
boolean sparse) |
static Tensor |
torch.embedding_bag(Tensor input,
Tensor weight) |
static Tensor |
torch.embedding_bag(Tensor input,
Tensor weight,
EmbeddingBagFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.embedding_bag
/** about the exact behavior of this functional.
|
static Tensor |
torch.embedding_bag(Tensor input,
Tensor weight,
Tensor offsets,
DoubleOptional max_norm,
double norm_type,
boolean scale_grad_by_freq,
EmbeddingBagMode mode,
boolean sparse,
Tensor per_sample_weights,
boolean include_last_offset,
LongOptional padding_idx) |
static Tensor |
torch.embedding_dense_backward(Tensor grad_output,
Tensor indices,
long num_weights,
long padding_idx,
boolean scale_grad_by_freq) |
static Tensor |
torch.embedding_renorm_(Tensor self,
Tensor indices,
double max_norm,
double norm_type) |
static Tensor |
torch.embedding_sparse_backward(Tensor grad,
Tensor indices,
long num_weights,
long padding_idx,
boolean scale_grad_by_freq) |
static Tensor |
torch.embedding(Tensor weight,
Tensor indices) |
static Tensor |
torch.embedding(Tensor input,
Tensor weight,
EmbeddingFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.embedding
/** about the exact behavior of this functional.
|
static Tensor |
torch.embedding(Tensor weight,
Tensor indices,
long padding_idx,
boolean scale_grad_by_freq,
boolean sparse) |
static Tensor |
torch.embedding(Tensor input,
Tensor weight,
LongOptional padding_idx,
DoubleOptional max_norm,
double norm_type,
boolean scale_grad_by_freq,
boolean sparse) |
static Tensor |
torch.empty_cpu(long[] size,
ScalarTypeOptional dtype_opt,
LayoutOptional layout_opt,
DeviceOptional device_opt,
BoolOptional pin_memory_opt,
MemoryFormatOptional memory_format_opt) |
static Tensor |
torch.empty_cpu(LongArrayRef size,
ScalarTypeOptional dtype_opt,
LayoutOptional layout_opt,
DeviceOptional device_opt,
BoolOptional pin_memory_opt,
MemoryFormatOptional memory_format_opt) |
static Tensor |
torch.empty_generic(long[] size,
Allocator allocator,
byte dispatch_key,
torch.ScalarType dtype,
Device device,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_generic(LongArrayRef size,
Allocator allocator,
torch.DispatchKey dispatch_key,
torch.ScalarType dtype,
Device device,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_like(Tensor self) |
static Tensor |
torch.empty_like(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_like(Tensor self,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_out(Tensor out,
long... size) |
static Tensor |
torch.empty_out(Tensor out,
long[] size,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_out(Tensor out,
LongArrayRef size) |
static Tensor |
torch.empty_out(Tensor out,
LongArrayRef size,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_outf(long[] size,
MemoryFormatOptional memory_format,
Tensor out) |
static Tensor |
torch.empty_outf(LongArrayRef size,
MemoryFormatOptional memory_format,
Tensor out) |
static Tensor |
torch.empty_quantized(long[] size,
Tensor qtensor) |
static Tensor |
torch.empty_quantized(long[] size,
Tensor qtensor,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_quantized(long[] size,
Tensor qtensor,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_quantized(LongArrayRef size,
Tensor qtensor) |
static Tensor |
torch.empty_quantized(LongArrayRef size,
Tensor qtensor,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_quantized(LongArrayRef size,
Tensor qtensor,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_strided(long[] size,
long... stride) |
static Tensor |
torch.empty_strided(long[] size,
long[] stride,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.empty_strided(long[] size,
long[] stride,
TensorOptions options) |
static Tensor |
torch.empty_strided(LongArrayRef size,
LongArrayRef stride) |
static Tensor |
torch.empty_strided(LongArrayRef size,
LongArrayRef stride,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.empty_strided(LongArrayRef size,
LongArrayRef stride,
TensorOptions options) |
static Tensor |
torch.empty(long... size) |
static Tensor |
torch.empty(long[] size,
DimnameListOptional names) |
static Tensor |
torch.empty(long[] size,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty(long[] size,
DimnameListOptional names,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty(long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty(long[] size,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty(LongArrayRef size) |
static Tensor |
torch.empty(LongArrayRef size,
DimnameListOptional names) |
static Tensor |
torch.empty(LongArrayRef size,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty(LongArrayRef size,
DimnameListOptional names,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty(LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty(LongArrayRef size,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.eq_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.eq_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.eq_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.eq_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.eq(Tensor self,
Scalar other) |
static Tensor |
torch.eq(Tensor self,
Tensor other) |
static Tensor |
torch.equals(Scalar x,
Tensor y) |
static Tensor |
torch.equals(Tensor x,
Scalar y) |
static Tensor |
torch.equals(Tensor x,
Tensor y) |
static Tensor |
torch.erf_(Tensor self) |
static Tensor |
torch.erf_out(Tensor out,
Tensor self) |
static Tensor |
torch.erf_outf(Tensor self,
Tensor out) |
static Tensor |
torch.erf(Tensor self) |
static Tensor |
torch.erfc_(Tensor self) |
static Tensor |
torch.erfc_out(Tensor out,
Tensor self) |
static Tensor |
torch.erfc_outf(Tensor self,
Tensor out) |
static Tensor |
torch.erfc(Tensor self) |
static Tensor |
torch.erfinv_out(Tensor out,
Tensor self) |
static Tensor |
torch.erfinv_outf(Tensor self,
Tensor out) |
static Tensor |
torch.erfinv(Tensor self) |
static Tensor |
torch.exp_(Tensor self) |
static Tensor |
torch.exp_out(Tensor out,
Tensor self) |
static Tensor |
torch.exp_outf(Tensor self,
Tensor out) |
static Tensor |
torch.exp(Tensor self) |
static Tensor |
torch.exp2_(Tensor self) |
static Tensor |
torch.exp2_out(Tensor out,
Tensor self) |
static Tensor |
torch.exp2_outf(Tensor self,
Tensor out) |
static Tensor |
torch.exp2(Tensor self) |
static Tensor |
torch.expm1_(Tensor self) |
static Tensor |
torch.expm1_out(Tensor out,
Tensor self) |
static Tensor |
torch.expm1_outf(Tensor self,
Tensor out) |
static Tensor |
torch.expm1(Tensor self) |
static Tensor |
torch.eye_(Tensor matrix)
Fills the given 2-dimensional
matrix with an identity matrix. |
static Tensor |
torch.eye_out(Tensor out,
long n) |
static Tensor |
torch.eye_out(Tensor out,
long n,
long m) |
static Tensor |
torch.eye_outf(long n,
long m,
Tensor out) |
static Tensor |
torch.eye_outf(long n,
Tensor out) |
static Tensor |
torch.eye(long n) |
static Tensor |
torch.eye(long n,
long m) |
static Tensor |
torch.eye(long n,
long m,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.eye(long n,
long m,
TensorOptions options) |
static Tensor |
torch.eye(long n,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.eye(long n,
TensorOptions options) |
static Tensor |
torch.fake_quantize_per_channel_affine_cachemask_backward(Tensor grad,
Tensor mask) |
static Tensor |
torch.fake_quantize_per_channel_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max) |
static Tensor |
torch.fake_quantize_per_tensor_affine_cachemask_backward(Tensor grad,
Tensor mask) |
static Tensor |
torch.fake_quantize_per_tensor_affine(Tensor self,
double scale,
long zero_point,
long quant_min,
long quant_max) |
static Tensor |
torch.fake_quantize_per_tensor_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long quant_min,
long quant_max) |
static Tensor |
torch.fbgemm_linear_fp16_weight_fp32_activation(Tensor input,
Tensor packed_weight,
Tensor bias) |
static Tensor |
torch.fbgemm_linear_fp16_weight(Tensor input,
Tensor packed_weight,
Tensor bias) |
static Tensor |
torch.fbgemm_linear_int8_weight_fp32_activation(Tensor input,
Tensor weight,
Tensor packed,
Tensor col_offsets,
Scalar weight_scale,
Scalar weight_zero_point,
Tensor bias) |
static Tensor |
torch.fbgemm_linear_int8_weight(Tensor input,
Tensor weight,
Tensor packed,
Tensor col_offsets,
Scalar weight_scale,
Scalar weight_zero_point,
Tensor bias) |
static Tensor |
torch.fbgemm_pack_gemm_matrix_fp16(Tensor input) |
static Tensor |
torch.fbgemm_pack_quantized_matrix(Tensor input) |
static Tensor |
torch.fbgemm_pack_quantized_matrix(Tensor input,
long K,
long N) |
static Tensor |
torch.feature_alpha_dropout_(Tensor self,
double p,
boolean train) |
static Tensor |
torch.feature_alpha_dropout(Tensor input) |
static Tensor |
torch.feature_alpha_dropout(Tensor input,
double p,
boolean train) |
static Tensor |
torch.feature_alpha_dropout(Tensor input,
double p,
boolean training,
boolean inplace) |
static Tensor |
torch.feature_alpha_dropout(Tensor input,
FeatureAlphaDropoutFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.feature_alpha_dropout
/** about the exact behavior of this functional.
|
static Tensor |
torch.feature_dropout_(Tensor self,
double p,
boolean train) |
static Tensor |
torch.feature_dropout(Tensor input,
double p,
boolean train) |
static Tensor |
torch.fft_fft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_fft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_fft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_fft(Tensor self) |
static Tensor |
torch.fft_fft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_fft2_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_fft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_fft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_fft2_outf(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_fft2_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_fft2(Tensor self) |
static Tensor |
torch.fft_fft2(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_fft2(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_fftfreq_out(Tensor out,
long n) |
static Tensor |
torch.fft_fftfreq_out(Tensor out,
long n,
double d) |
static Tensor |
torch.fft_fftfreq_outf(long n,
double d,
Tensor out) |
static Tensor |
torch.fft_fftfreq(long n) |
static Tensor |
torch.fft_fftfreq(long n,
double d,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.fft_fftfreq(long n,
double d,
TensorOptions options) |
static Tensor |
torch.fft_fftn_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_fftn_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_fftn_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_fftn(Tensor self) |
static Tensor |
torch.fft_fftn(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_fftshift(Tensor self) |
static Tensor |
torch.fft_fftshift(Tensor self,
LongArrayRefOptional dim) |
static Tensor |
torch.fft_hfft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_hfft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_hfft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_hfft(Tensor self) |
static Tensor |
torch.fft_hfft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_ifft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_ifft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_ifft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_ifft(Tensor self) |
static Tensor |
torch.fft_ifft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_ifft2_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_ifft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_ifft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_ifft2_outf(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_ifft2_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_ifft2(Tensor self) |
static Tensor |
torch.fft_ifft2(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_ifft2(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_ifftn_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_ifftn_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_ifftn_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_ifftn(Tensor self) |
static Tensor |
torch.fft_ifftn(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_ifftshift(Tensor self) |
static Tensor |
torch.fft_ifftshift(Tensor self,
LongArrayRefOptional dim) |
static Tensor |
torch.fft_ihfft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_ihfft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_ihfft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_ihfft(Tensor self) |
static Tensor |
torch.fft_ihfft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_irfft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_irfft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_irfft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_irfft(Tensor self) |
static Tensor |
torch.fft_irfft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_irfft2_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_irfft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_irfft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_irfft2_outf(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_irfft2_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_irfft2(Tensor self) |
static Tensor |
torch.fft_irfft2(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_irfft2(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_irfftn_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_irfftn_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_irfftn_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_irfftn(Tensor self) |
static Tensor |
torch.fft_irfftn(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_rfft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_rfft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_rfft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_rfft(Tensor self) |
static Tensor |
torch.fft_rfft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_rfft2_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_rfft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_rfft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_rfft2_outf(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_rfft2_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_rfft2(Tensor self) |
static Tensor |
torch.fft_rfft2(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_rfft2(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_rfftfreq_out(Tensor out,
long n) |
static Tensor |
torch.fft_rfftfreq_out(Tensor out,
long n,
double d) |
static Tensor |
torch.fft_rfftfreq_outf(long n,
double d,
Tensor out) |
static Tensor |
torch.fft_rfftfreq(long n) |
static Tensor |
torch.fft_rfftfreq(long n,
double d,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.fft_rfftfreq(long n,
double d,
TensorOptions options) |
static Tensor |
torch.fft_rfftn_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_rfftn_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_rfftn_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_rfftn(Tensor self) |
static Tensor |
torch.fft_rfftn(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fill_(Tensor self,
Scalar value) |
static Tensor |
torch.fill_(Tensor self,
Tensor value) |
static Tensor |
torch.fix_(Tensor self) |
static Tensor |
torch.fix_out(Tensor out,
Tensor self) |
static Tensor |
torch.fix_outf(Tensor self,
Tensor out) |
static Tensor |
torch.fix(Tensor self) |
static Tensor |
torch.flatten_dense_tensors(TensorArrayRef tensors) |
static Tensor |
torch.flatten(Tensor self) |
static Tensor |
torch.flatten(Tensor self,
DimnameArrayRef dims,
Dimname out_dim) |
static Tensor |
torch.flatten(Tensor self,
Dimname start_dim,
Dimname end_dim,
Dimname out_dim) |
static Tensor |
torch.flatten(Tensor self,
long start_dim,
long end_dim) |
static Tensor |
torch.flatten(Tensor self,
long start_dim,
long end_dim,
Dimname out_dim) |
static Tensor |
torch.flip(Tensor self,
long... dims) |
static Tensor |
torch.flip(Tensor self,
LongArrayRef dims) |
static Tensor |
torch.fliplr(Tensor self) |
static Tensor |
torch.flipud(Tensor self) |
static Tensor |
torch.float_power_out(Tensor out,
Scalar self,
Tensor exponent) |
static Tensor |
torch.float_power_out(Tensor out,
Tensor self,
Scalar exponent) |
static Tensor |
torch.float_power_out(Tensor out,
Tensor self,
Tensor exponent) |
static Tensor |
torch.float_power_outf(Scalar self,
Tensor exponent,
Tensor out) |
static Tensor |
torch.float_power_outf(Tensor self,
Scalar exponent,
Tensor out) |
static Tensor |
torch.float_power_outf(Tensor self,
Tensor exponent,
Tensor out) |
static Tensor |
torch.float_power(Scalar self,
Tensor exponent) |
static Tensor |
torch.float_power(Tensor self,
Scalar exponent) |
static Tensor |
torch.float_power(Tensor self,
Tensor exponent) |
static Tensor |
torch.floor_(Tensor self) |
static Tensor |
torch.floor_divide_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.floor_divide_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.floor_divide(Tensor self,
Scalar other) |
static Tensor |
torch.floor_divide(Tensor self,
Tensor other) |
static Tensor |
torch.floor_out(Tensor out,
Tensor self) |
static Tensor |
torch.floor_outf(Tensor self,
Tensor out) |
static Tensor |
torch.floor(Tensor self) |
static Tensor |
torch.fmax_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.fmax_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.fmax(Tensor self,
Tensor other) |
static Tensor |
torch.fmin_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.fmin_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.fmin(Tensor self,
Tensor other) |
static Tensor |
torch.fmod_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.fmod_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.fmod_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.fmod_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.fmod(Tensor self,
Scalar other) |
static Tensor |
torch.fmod(Tensor self,
Tensor other) |
static Tensor |
torch.fold(Tensor input,
FoldOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.fold
/** about the exact behavior of this functional.
|
static Tensor |
torch.fold(Tensor input,
LongPointer output_size,
LongPointer kernel_size,
LongPointer dilation,
LongPointer padding,
LongPointer stride) |
static Tensor |
torch.frac_(Tensor self) |
static Tensor |
torch.frac_out(Tensor out,
Tensor self) |
static Tensor |
torch.frac_outf(Tensor self,
Tensor out) |
static Tensor |
torch.frac(Tensor self) |
static Tensor |
torch.fractional_max_pool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool2d_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.fractional_max_pool2d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.fractional_max_pool2d_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool2d_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool2d(Tensor input,
FractionalMaxPool2dOptions options)
See the documentation for
torch::nn::functional::FractionalMaxPool2dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static Tensor |
torch.fractional_max_pool2d(Tensor input,
LongPointer kernel_size,
LongExpandingArrayOptional output_size,
DoubleExpandingArrayOptional output_ratio,
Tensor _random_samples) |
static Tensor |
torch.fractional_max_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool3d_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.fractional_max_pool3d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.fractional_max_pool3d_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool3d_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool3d(Tensor input,
FractionalMaxPool3dOptions options)
See the documentation for
torch::nn::functional::FractionalMaxPool3dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static Tensor |
torch.fractional_max_pool3d(Tensor input,
LongPointer kernel_size,
LongExpandingArrayOptional output_size,
DoubleExpandingArrayOptional output_ratio,
Tensor _random_samples) |
static Tensor |
torch.frobenius_norm_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.frobenius_norm_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.frobenius_norm_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.frobenius_norm_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.frobenius_norm_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.frobenius_norm_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.frobenius_norm(Tensor self) |
static Tensor |
torch.frobenius_norm(Tensor self,
long... dim) |
static Tensor |
torch.frobenius_norm(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.frobenius_norm(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.frobenius_norm(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.from_blob(Pointer data,
long... sizes) |
static Tensor |
torch.from_blob(Pointer data,
long[] sizes,
long... strides) |
static Tensor |
torch.from_blob(Pointer data,
long[] sizes,
long[] strides,
TensorOptions options) |
static Tensor |
torch.from_blob(Pointer data,
long[] sizes,
long[] strides,
torch.Deleter deleter) |
static Tensor |
torch.from_blob(Pointer data,
long[] sizes,
long[] strides,
torch.Deleter deleter,
TensorOptions options) |
static Tensor |
torch.from_blob(Pointer data,
long[] sizes,
long[] strides,
torch.Deleter deleter,
TensorOptions options,
DeviceOptional target_device) |
static Tensor |
torch.from_blob(Pointer data,
long[] sizes,
TensorOptions options) |
static Tensor |
torch.from_blob(Pointer data,
long[] sizes,
torch.Deleter deleter) |
static Tensor |
torch.from_blob(Pointer data,
long[] sizes,
torch.Deleter deleter,
TensorOptions options) |
static Tensor |
torch.from_blob(Pointer data,
LongArrayRef sizes) |
static Tensor |
torch.from_blob(Pointer data,
LongArrayRef sizes,
LongArrayRef strides) |
static Tensor |
torch.from_blob(Pointer data,
LongArrayRef sizes,
LongArrayRef strides,
TensorOptions options) |
static Tensor |
torch.from_blob(Pointer data,
LongArrayRef sizes,
LongArrayRef strides,
torch.Deleter deleter) |
static Tensor |
torch.from_blob(Pointer data,
LongArrayRef sizes,
LongArrayRef strides,
torch.Deleter deleter,
TensorOptions options)
Exposes the given
data as a Tensor without taking ownership of the
original data. |
static Tensor |
torch.from_blob(Pointer data,
LongArrayRef sizes,
LongArrayRef strides,
torch.Deleter deleter,
TensorOptions options,
DeviceOptional target_device) |
static Tensor |
torch.from_blob(Pointer data,
LongArrayRef sizes,
TensorOptions options) |
static Tensor |
torch.from_blob(Pointer data,
LongArrayRef sizes,
torch.Deleter deleter) |
static Tensor |
torch.from_blob(Pointer data,
LongArrayRef sizes,
torch.Deleter deleter,
TensorOptions options) |
static Tensor |
torch.from_file(Pointer filename) |
static Tensor |
torch.from_file(Pointer filename,
BoolOptional shared,
LongOptional size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.from_file(Pointer filename,
BoolOptional shared,
LongOptional size,
TensorOptions options) |
static Tensor |
torch.full_like(Tensor self,
Scalar fill_value) |
static Tensor |
torch.full_like(Tensor self,
Scalar fill_value,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.full_like(Tensor self,
Scalar fill_value,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.full_out(Tensor out,
long[] size,
Scalar fill_value) |
static Tensor |
torch.full_out(Tensor out,
LongArrayRef size,
Scalar fill_value) |
static Tensor |
torch.full_outf(long[] size,
Scalar fill_value,
Tensor out) |
static Tensor |
torch.full_outf(LongArrayRef size,
Scalar fill_value,
Tensor out) |
static Tensor |
torch.full(long[] size,
Scalar fill_value) |
static Tensor |
torch.full(long[] size,
Scalar fill_value,
DimnameListOptional names) |
static Tensor |
torch.full(long[] size,
Scalar fill_value,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.full(long[] size,
Scalar fill_value,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.full(long[] size,
Scalar fill_value,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.full(long[] size,
Scalar fill_value,
TensorOptions options) |
static Tensor |
torch.full(LongArrayRef size,
Scalar fill_value) |
static Tensor |
torch.full(LongArrayRef size,
Scalar fill_value,
DimnameListOptional names) |
static Tensor |
torch.full(LongArrayRef size,
Scalar fill_value,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.full(LongArrayRef size,
Scalar fill_value,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.full(LongArrayRef size,
Scalar fill_value,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.full(LongArrayRef size,
Scalar fill_value,
TensorOptions options) |
static Tensor |
torch.fused_moving_avg_obs_fake_quant(Tensor self,
Tensor observer_on,
Tensor fake_quant_on,
Tensor running_min,
Tensor running_max,
Tensor scale,
Tensor zero_point,
double averaging_const,
long quant_min,
long quant_max,
long ch_axis) |
static Tensor |
torch.fused_moving_avg_obs_fake_quant(Tensor self,
Tensor observer_on,
Tensor fake_quant_on,
Tensor running_min,
Tensor running_max,
Tensor scale,
Tensor zero_point,
double averaging_const,
long quant_min,
long quant_max,
long ch_axis,
boolean per_row_fake_quant,
boolean symmetric_quant) |
static Tensor |
torch.gather_backward(Tensor grad,
Tensor self,
long dim,
Tensor index,
boolean sparse_grad) |
static Tensor |
torch.gather_out(Tensor out,
Tensor self,
Dimname dim,
Tensor index) |
static Tensor |
torch.gather_out(Tensor out,
Tensor self,
Dimname dim,
Tensor index,
boolean sparse_grad) |
static Tensor |
torch.gather_out(Tensor out,
Tensor self,
long dim,
Tensor index) |
static Tensor |
torch.gather_out(Tensor out,
Tensor self,
long dim,
Tensor index,
boolean sparse_grad) |
static Tensor |
torch.gather_outf(Tensor self,
Dimname dim,
Tensor index,
boolean sparse_grad,
Tensor out) |
static Tensor |
torch.gather_outf(Tensor self,
long dim,
Tensor index,
boolean sparse_grad,
Tensor out) |
static Tensor |
torch.gather(Tensor self,
Dimname dim,
Tensor index) |
static Tensor |
torch.gather(Tensor self,
Dimname dim,
Tensor index,
boolean sparse_grad) |
static Tensor |
torch.gather(Tensor self,
long dim,
Tensor index) |
static Tensor |
torch.gather(Tensor self,
long dim,
Tensor index,
boolean sparse_grad) |
static Tensor |
torch.gcd_(Tensor self,
Tensor other) |
static Tensor |
torch.gcd_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.gcd_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.gcd(Tensor self,
Tensor other) |
static Tensor |
torch.ge_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.ge_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.ge_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.ge_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.ge(Tensor self,
Scalar other) |
static Tensor |
torch.ge(Tensor self,
Tensor other) |
static Tensor |
torch.gelu_backward_out(Tensor grad_input,
Tensor grad,
Tensor self) |
static Tensor |
torch.gelu_backward_outf(Tensor grad,
Tensor self,
Tensor grad_input) |
static Tensor |
torch.gelu_backward(Tensor grad,
Tensor self) |
static Tensor |
torch.gelu_out(Tensor out,
Tensor self) |
static Tensor |
torch.gelu_outf(Tensor self,
Tensor out) |
static Tensor |
torch.gelu(Tensor self) |
static Tensor |
torch.ger_out(Tensor out,
Tensor self,
Tensor vec2) |
static Tensor |
torch.ger_outf(Tensor self,
Tensor vec2,
Tensor out) |
static Tensor |
torch.ger(Tensor self,
Tensor vec2) |
static Tensor |
torch.get_item(Tensor self,
TensorIndexArrayRef indices) |
static Tensor |
torch.get_item(Tensor self,
TensorIndexArrayRef indices,
boolean disable_slice_optimization) |
static Tensor |
torch.getSizeOf(Tensor var,
long dim) |
static Tensor |
torch.glu_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long dim) |
static Tensor |
torch.glu_backward_outf(Tensor grad_output,
Tensor self,
long dim,
Tensor grad_input) |
static Tensor |
torch.glu_backward(Tensor grad_output,
Tensor self,
long dim) |
static Tensor |
torch.glu_out(Tensor out,
Tensor self) |
static Tensor |
torch.glu_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.glu_outf(Tensor self,
long dim,
Tensor out) |
static Tensor |
torch.glu(Tensor self) |
static Tensor |
torch.glu(Tensor input,
GLUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.glu
/** about the exact behavior of this functional.
|
static Tensor |
torch.glu(Tensor self,
long dim) |
static Tensor |
torch.greater_equal_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.greater_equal_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.greater_equal_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.greater_equal_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.greater_equal(Tensor self,
Scalar other) |
static Tensor |
torch.greater_equal(Tensor self,
Tensor other) |
static Tensor |
torch.greater_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.greater_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.greater_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.greater_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.greater(Tensor self,
Scalar other) |
static Tensor |
torch.greater(Tensor self,
Tensor other) |
static Tensor |
torch.greaterThan(Scalar x,
Tensor y) |
static Tensor |
torch.greaterThan(Tensor x,
Scalar y) |
static Tensor |
torch.greaterThan(Tensor x,
Tensor y) |
static Tensor |
torch.greaterThanEquals(Scalar x,
Tensor y) |
static Tensor |
torch.greaterThanEquals(Tensor x,
Scalar y) |
static Tensor |
torch.greaterThanEquals(Tensor x,
Tensor y) |
static Tensor |
torch.grid_sample(Tensor input,
Tensor grid) |
static Tensor |
torch.grid_sample(Tensor input,
Tensor grid,
grid_sample_mode_t mode,
grid_sample_padding_mode_t padding_mode,
BoolOptional align_corners) |
static Tensor |
torch.grid_sample(Tensor input,
Tensor grid,
GridSampleFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.grid_sample
/** about the exact behavior of this functional.
|
static Tensor |
torch.grid_sampler_2d(Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static Tensor |
torch.grid_sampler_3d(Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static Tensor |
torch.grid_sampler(Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static Tensor |
torch.group_norm(Tensor input,
GroupNormFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.group_norm
/** about the exact behavior of this functional.
|
static Tensor |
torch.group_norm(Tensor input,
long num_groups) |
static Tensor |
torch.group_norm(Tensor input,
long num_groups,
TensorOptional weight,
TensorOptional bias,
double eps,
boolean cudnn_enabled) |
static Tensor |
torch.group_norm(Tensor input,
long num_groups,
Tensor weight,
Tensor bias,
double eps) |
static Tensor |
torch.gru_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh) |
static Tensor |
torch.gru_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static Tensor |
torch.gt_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.gt_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.gt_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.gt_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.gt(Tensor self,
Scalar other) |
static Tensor |
torch.gt(Tensor self,
Tensor other) |
static Tensor |
torch.gumbel_softmax(Tensor logits) |
static Tensor |
torch.gumbel_softmax(Tensor logits,
double tau,
boolean hard,
int dim) |
static Tensor |
torch.gumbel_softmax(Tensor logits,
GumbelSoftmaxFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.gumbel_softmax
/** about the exact behavior of this functional.
|
static Tensor |
torch.hamming_window(long window_length) |
static Tensor |
torch.hamming_window(long window_length,
boolean periodic) |
static Tensor |
torch.hamming_window(long window_length,
boolean periodic,
double alpha) |
static Tensor |
torch.hamming_window(long window_length,
boolean periodic,
double alpha,
double beta) |
static Tensor |
torch.hamming_window(long window_length,
boolean periodic,
double alpha,
double beta,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.hamming_window(long window_length,
boolean periodic,
double alpha,
double beta,
TensorOptions options) |
static Tensor |
torch.hamming_window(long window_length,
boolean periodic,
double alpha,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.hamming_window(long window_length,
boolean periodic,
double alpha,
TensorOptions options) |
static Tensor |
torch.hamming_window(long window_length,
boolean periodic,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.hamming_window(long window_length,
boolean periodic,
TensorOptions options) |
static Tensor |
torch.hamming_window(long window_length,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.hamming_window(long window_length,
TensorOptions options) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
long[] dim_ptr,
long[] specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
long... prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
long[] dim_ptr,
long[] specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
LongArrayRef prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
LongBuffer dim_ptr,
LongBuffer specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
long... prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
LongBuffer dim_ptr,
LongBuffer specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
LongArrayRef prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
LongPointer dim_ptr,
LongPointer specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
long... prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
LongPointer dim_ptr,
LongPointer specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
LongArrayRef prev_dim_result_sizes) |
static Tensor |
torch.hann_window(long window_length) |
static Tensor |
torch.hann_window(long window_length,
boolean periodic) |
static Tensor |
torch.hann_window(long window_length,
boolean periodic,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.hann_window(long window_length,
boolean periodic,
TensorOptions options) |
static Tensor |
torch.hann_window(long window_length,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.hann_window(long window_length,
TensorOptions options) |
static Tensor |
torch.hardshrink_backward_out(Tensor grad_input,
Tensor grad_out,
Tensor self,
Scalar lambd) |
static Tensor |
torch.hardshrink_backward_outf(Tensor grad_out,
Tensor self,
Scalar lambd,
Tensor grad_input) |
static Tensor |
torch.hardshrink_backward(Tensor grad_out,
Tensor self,
Scalar lambd) |
static Tensor |
torch.hardshrink_out(Tensor out,
Tensor self) |
static Tensor |
torch.hardshrink_out(Tensor out,
Tensor self,
Scalar lambd) |
static Tensor |
torch.hardshrink_outf(Tensor self,
Scalar lambd,
Tensor out) |
static Tensor |
torch.hardshrink(Tensor self) |
static Tensor |
torch.hardshrink(Tensor input,
double lambda) |
static Tensor |
torch.hardshrink(Tensor input,
HardshrinkOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.hardshrink
/** about the exact behavior of this functional.
|
static Tensor |
torch.hardshrink(Tensor self,
Scalar lambd) |
static Tensor |
torch.hardsigmoid_(Tensor self) |
static Tensor |
torch.hardsigmoid_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self) |
static Tensor |
torch.hardsigmoid_backward_outf(Tensor grad_output,
Tensor self,
Tensor grad_input) |
static Tensor |
torch.hardsigmoid_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.hardsigmoid_out(Tensor out,
Tensor self) |
static Tensor |
torch.hardsigmoid_outf(Tensor self,
Tensor out) |
static Tensor |
torch.hardsigmoid(Tensor self) |
static Tensor |
torch.hardswish_(Tensor self) |
static Tensor |
torch.hardswish_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.hardswish_out(Tensor out,
Tensor self) |
static Tensor |
torch.hardswish_outf(Tensor self,
Tensor out) |
static Tensor |
torch.hardswish(Tensor self) |
static Tensor |
torch.hardtanh_(Tensor self) |
static Tensor |
torch.hardtanh_(Tensor self,
Scalar min_val,
Scalar max_val) |
static Tensor |
torch.hardtanh_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Scalar min_val,
Scalar max_val) |
static Tensor |
torch.hardtanh_backward_outf(Tensor grad_output,
Tensor self,
Scalar min_val,
Scalar max_val,
Tensor grad_input) |
static Tensor |
torch.hardtanh_backward(Tensor grad_output,
Tensor self,
Scalar min_val,
Scalar max_val) |
static Tensor |
torch.hardtanh_out(Tensor out,
Tensor self) |
static Tensor |
torch.hardtanh_out(Tensor out,
Tensor self,
Scalar min_val,
Scalar max_val) |
static Tensor |
torch.hardtanh_outf(Tensor self,
Scalar min_val,
Scalar max_val,
Tensor out) |
static Tensor |
torch.hardtanh(Tensor self) |
static Tensor |
torch.hardtanh(Tensor input,
double min_val,
double max_val,
boolean inplace) |
static Tensor |
torch.hardtanh(Tensor input,
HardtanhOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.hardtanh
/** about the exact behavior of this functional.
|
static Tensor |
torch.hardtanh(Tensor self,
Scalar min_val,
Scalar max_val) |
static Tensor |
torch.heaviside_out(Tensor out,
Tensor self,
Tensor values) |
static Tensor |
torch.heaviside_outf(Tensor self,
Tensor values,
Tensor out) |
static Tensor |
torch.heaviside(Tensor self,
Tensor values) |
static Tensor |
torch.hinge_embedding_loss(Tensor self,
Tensor target) |
static Tensor |
torch.hinge_embedding_loss(Tensor self,
Tensor target,
double margin,
long reduction) |
static Tensor |
torch.hinge_embedding_loss(Tensor input,
Tensor target,
double margin,
loss_reduction_t reduction) |
static Tensor |
torch.hinge_embedding_loss(Tensor input,
Tensor target,
HingeEmbeddingLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.hinge_embedding_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.histc_out(Tensor out,
Tensor self) |
static Tensor |
torch.histc_out(Tensor out,
Tensor self,
long bins,
Scalar min,
Scalar max) |
static Tensor |
torch.histc_outf(Tensor self,
long bins,
Scalar min,
Scalar max,
Tensor out) |
static Tensor |
torch.histc(Tensor self) |
static Tensor |
torch.histc(Tensor self,
long bins,
Scalar min,
Scalar max) |
static Tensor |
torch.hspmm_out(Tensor out,
Tensor mat1,
Tensor mat2) |
static Tensor |
torch.hspmm_outf(Tensor mat1,
Tensor mat2,
Tensor out) |
static Tensor |
torch.hspmm(Tensor mat1,
Tensor mat2) |
static Tensor |
torch.hstack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.hstack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.hstack(TensorArrayRef tensors) |
static Tensor |
torch.huber_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double delta) |
static Tensor |
torch.huber_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double delta,
Tensor grad_input) |
static Tensor |
torch.huber_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double delta) |
static Tensor |
torch.huber_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.huber_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction,
double delta) |
static Tensor |
torch.huber_loss_outf(Tensor self,
Tensor target,
long reduction,
double delta,
Tensor out) |
static Tensor |
torch.huber_loss(Tensor self,
Tensor target) |
static Tensor |
torch.huber_loss(Tensor input,
Tensor target,
HuberLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.huber_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.huber_loss(Tensor self,
Tensor target,
long reduction,
double delta) |
static Tensor |
torch.huber_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.huber_loss(Tensor input,
Tensor target,
loss_reduction_t reduction,
double delta) |
static Tensor |
torch.hypot_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.hypot_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.hypot(Tensor self,
Tensor other) |
static Tensor |
torch.i0_(Tensor self) |
static Tensor |
torch.i0_out(Tensor out,
Tensor self) |
static Tensor |
torch.i0_outf(Tensor self,
Tensor out) |
static Tensor |
torch.i0(Tensor self) |
static Tensor |
torch.igamma_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.igamma_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.igamma(Tensor self,
Tensor other) |
static Tensor |
torch.igammac_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.igammac_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.igammac(Tensor self,
Tensor other) |
static Tensor |
torch.im2col_backward_out(Tensor grad_input,
Tensor grad_output,
long[] input_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.im2col_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef input_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.im2col_backward_outf(Tensor grad_output,
long[] input_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long[] stride,
Tensor grad_input) |
static Tensor |
torch.im2col_backward_outf(Tensor grad_output,
LongArrayRef input_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride,
Tensor grad_input) |
static Tensor |
torch.im2col_backward(Tensor grad_output,
long[] input_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.im2col_backward(Tensor grad_output,
LongArrayRef input_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.im2col_out(Tensor out,
Tensor self,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.im2col_out(Tensor out,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.im2col_outf(Tensor self,
long[] kernel_size,
long[] dilation,
long[] padding,
long[] stride,
Tensor out) |
static Tensor |
torch.im2col_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride,
Tensor out) |
static Tensor |
torch.im2col(Tensor self,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.im2col(Tensor self,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.imag(Tensor self) |
static Tensor |
torch.index_add(Tensor self,
Dimname dim,
Tensor index,
Tensor source) |
static Tensor |
torch.index_add(Tensor self,
Dimname dim,
Tensor index,
Tensor source,
Scalar alpha) |
static Tensor |
torch.index_add(Tensor self,
long dim,
Tensor index,
Tensor source) |
static Tensor |
torch.index_add(Tensor self,
long dim,
Tensor index,
Tensor source,
Scalar alpha) |
static Tensor |
torch.index_copy(Tensor self,
Dimname dim,
Tensor index,
Tensor source) |
static Tensor |
torch.index_copy(Tensor self,
long dim,
Tensor index,
Tensor source) |
static Tensor |
torch.index_fill(Tensor self,
Dimname dim,
Tensor index,
Scalar value) |
static Tensor |
torch.index_fill(Tensor self,
Dimname dim,
Tensor index,
Tensor value) |
static Tensor |
torch.index_fill(Tensor self,
long dim,
Tensor index,
Scalar value) |
static Tensor |
torch.index_fill(Tensor self,
long dim,
Tensor index,
Tensor value) |
static Tensor |
torch.index_select_backward(Tensor grad,
long[] self_sizes,
long dim,
Tensor index) |
static Tensor |
torch.index_select_backward(Tensor grad,
LongArrayRef self_sizes,
long dim,
Tensor index) |
static Tensor |
torch.index_select_out(Tensor out,
Tensor self,
Dimname dim,
Tensor index) |
static Tensor |
torch.index_select_out(Tensor out,
Tensor self,
long dim,
Tensor index) |
static Tensor |
torch.index_select_outf(Tensor self,
Dimname dim,
Tensor index,
Tensor out) |
static Tensor |
torch.index_select_outf(Tensor self,
long dim,
Tensor index,
Tensor out) |
static Tensor |
torch.index_select(Tensor self,
Dimname dim,
Tensor index) |
static Tensor |
torch.index_select(Tensor self,
long dim,
Tensor index) |
static Tensor |
torch.infinitely_differentiable_gelu_backward(Tensor grad,
Tensor self) |
static Tensor |
torch.inner_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.inner_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.inner(Tensor self,
Tensor other) |
static Tensor |
torch.instance_norm(Tensor input) |
static Tensor |
torch.instance_norm(Tensor input,
InstanceNormFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.instance_norm
/** about the exact behavior of this functional.
|
static Tensor |
torch.instance_norm(Tensor input,
TensorOptional weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean use_input_stats,
double momentum,
double eps,
boolean cudnn_enabled) |
static Tensor |
torch.instance_norm(Tensor input,
Tensor running_mean,
Tensor running_var,
Tensor weight,
Tensor bias,
boolean use_input_stats,
double momentum,
double eps) |
static Tensor |
torch.int_repr(Tensor self) |
static Tensor |
torch.interpolate(Tensor input) |
static Tensor |
torch.interpolate(Tensor input,
InterpolateFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.interpolate
/** about the exact behavior of this functional.
|
static Tensor |
torch.interpolate(Tensor input,
LongVectorOptional size,
DoubleVectorOptional scale_factor,
interpolate_mode_t mode,
BoolOptional align_corners,
BoolOptional recompute_scale_factor) |
static Tensor |
torch.inverse_out(Tensor out,
Tensor self) |
static Tensor |
torch.inverse_outf(Tensor self,
Tensor out) |
static Tensor |
torch.inverse(Tensor self) |
static Tensor |
torch.invert_permutation(Tensor permutation) |
static Tensor |
torch.isclose(Tensor self,
Tensor other) |
static Tensor |
torch.isclose(Tensor self,
Tensor other,
double rtol,
double atol,
boolean equal_nan) |
static Tensor |
torch.isfinite(Tensor self) |
static Tensor |
torch.isin_out(Tensor out,
Scalar element,
Tensor test_elements) |
static Tensor |
torch.isin_out(Tensor out,
Scalar element,
Tensor test_elements,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isin_out(Tensor out,
Tensor elements,
Scalar test_element) |
static Tensor |
torch.isin_out(Tensor out,
Tensor elements,
Scalar test_element,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isin_out(Tensor out,
Tensor elements,
Tensor test_elements) |
static Tensor |
torch.isin_out(Tensor out,
Tensor elements,
Tensor test_elements,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isin_outf(Scalar element,
Tensor test_elements,
boolean assume_unique,
boolean invert,
Tensor out) |
static Tensor |
torch.isin_outf(Tensor elements,
Scalar test_element,
boolean assume_unique,
boolean invert,
Tensor out) |
static Tensor |
torch.isin_outf(Tensor elements,
Tensor test_elements,
boolean assume_unique,
boolean invert,
Tensor out) |
static Tensor |
torch.isin(Scalar element,
Tensor test_elements) |
static Tensor |
torch.isin(Scalar element,
Tensor test_elements,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isin(Tensor elements,
Scalar test_element) |
static Tensor |
torch.isin(Tensor elements,
Scalar test_element,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isin(Tensor elements,
Tensor test_elements) |
static Tensor |
torch.isin(Tensor elements,
Tensor test_elements,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isinf(Tensor self) |
static Tensor |
torch.isnan(Tensor self) |
static Tensor |
torch.isneginf_out(Tensor out,
Tensor self) |
static Tensor |
torch.isneginf_outf(Tensor self,
Tensor out) |
static Tensor |
torch.isneginf(Tensor self) |
static Tensor |
torch.isposinf_out(Tensor out,
Tensor self) |
static Tensor |
torch.isposinf_outf(Tensor self,
Tensor out) |
static Tensor |
torch.isposinf(Tensor self) |
static Tensor |
torch.isreal(Tensor self) |
static Tensor |
torch.istft(Tensor self,
long n_fft) |
static Tensor |
torch.istft(Tensor self,
long n_fft,
LongOptional hop_length,
LongOptional win_length,
TensorOptional window,
boolean center,
boolean normalized,
BoolOptional onesided,
LongOptional length,
boolean return_complex) |
static Tensor |
torch.kaiming_normal_(Tensor tensor) |
static Tensor |
torch.kaiming_normal_(Tensor tensor,
double a,
FanModeType mode,
NonlinearityType nonlinearity)
Fills the input
Tensor with values according to the method
described in "Delving deep into rectifiers: Surpassing human-level
performance on ImageNet classification" - He, K. |
static Tensor |
torch.kaiming_uniform_(Tensor tensor) |
static Tensor |
torch.kaiming_uniform_(Tensor tensor,
double a,
FanModeType mode,
NonlinearityType nonlinearity)
Fills the input
Tensor with values according to the method
described in "Delving deep into rectifiers: Surpassing human-level
performance on ImageNet classification" - He, K. |
static Tensor |
torch.kaiser_window(long window_length) |
static Tensor |
torch.kaiser_window(long window_length,
boolean periodic) |
static Tensor |
torch.kaiser_window(long window_length,
boolean periodic,
double beta) |
static Tensor |
torch.kaiser_window(long window_length,
boolean periodic,
double beta,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.kaiser_window(long window_length,
boolean periodic,
double beta,
TensorOptions options) |
static Tensor |
torch.kaiser_window(long window_length,
boolean periodic,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.kaiser_window(long window_length,
boolean periodic,
TensorOptions options) |
static Tensor |
torch.kaiser_window(long window_length,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.kaiser_window(long window_length,
TensorOptions options) |
static Tensor |
torch.kl_div_backward(Tensor grad_output,
Tensor self,
Tensor target) |
static Tensor |
torch.kl_div_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
boolean log_target) |
static Tensor |
torch.kl_div(Tensor self,
Tensor target) |
static Tensor |
torch.kl_div(Tensor input,
Tensor target,
kldiv_loss_reduction_t reduction) |
static Tensor |
torch.kl_div(Tensor input,
Tensor target,
kldiv_loss_reduction_t reduction,
boolean log_target) |
static Tensor |
torch.kl_div(Tensor input,
Tensor target,
KLDivLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.kl_div
/** about the exact behavior of this functional.
|
static Tensor |
torch.kl_div(Tensor self,
Tensor target,
long reduction,
boolean log_target) |
static Tensor |
torch.kron_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.kron_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.kron(Tensor self,
Tensor other) |
static Tensor |
torch.l1_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.l1_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor grad_input) |
static Tensor |
torch.l1_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.l1_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.l1_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.l1_loss_outf(Tensor self,
Tensor target,
long reduction,
Tensor out) |
static Tensor |
torch.l1_loss(Tensor self,
Tensor target) |
static Tensor |
torch.l1_loss(Tensor input,
Tensor target,
L1LossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.l1_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.l1_loss(Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.l1_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.layer_norm(Tensor input,
LayerNormFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.layer_norm
/** about the exact behavior of this functional.
|
static Tensor |
torch.layer_norm(Tensor input,
long... normalized_shape) |
static Tensor |
torch.layer_norm(Tensor input,
long[] normalized_shape,
TensorOptional weight,
TensorOptional bias,
double eps,
boolean cudnn_enable) |
static Tensor |
torch.layer_norm(Tensor input,
LongArrayRef normalized_shape) |
static Tensor |
torch.layer_norm(Tensor input,
LongArrayRef normalized_shape,
TensorOptional weight,
TensorOptional bias,
double eps,
boolean cudnn_enable) |
static Tensor |
torch.layer_norm(Tensor input,
LongVector normalized_shape,
Tensor weight,
Tensor bias,
double eps) |
static Tensor |
torch.lcm_(Tensor self,
Tensor other) |
static Tensor |
torch.lcm_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.lcm_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.lcm(Tensor self,
Tensor other) |
static Tensor |
torch.ldexp_(Tensor self,
Tensor other) |
static Tensor |
torch.ldexp_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.ldexp_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.ldexp(Tensor self,
Tensor other) |
static Tensor |
torch.le_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.le_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.le_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.le_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.le(Tensor self,
Scalar other) |
static Tensor |
torch.le(Tensor self,
Tensor other) |
static Tensor |
torch.leaky_relu_(Tensor self) |
static Tensor |
torch.leaky_relu_(Tensor self,
Scalar negative_slope) |
static Tensor |
torch.leaky_relu_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Scalar negative_slope,
boolean self_is_result) |
static Tensor |
torch.leaky_relu_backward_outf(Tensor grad_output,
Tensor self,
Scalar negative_slope,
boolean self_is_result,
Tensor grad_input) |
static Tensor |
torch.leaky_relu_backward(Tensor grad_output,
Tensor self,
Scalar negative_slope,
boolean self_is_result) |
static Tensor |
torch.leaky_relu_out(Tensor out,
Tensor self) |
static Tensor |
torch.leaky_relu_out(Tensor out,
Tensor self,
Scalar negative_slope) |
static Tensor |
torch.leaky_relu_outf(Tensor self,
Scalar negative_slope,
Tensor out) |
static Tensor |
torch.leaky_relu(Tensor self) |
static Tensor |
torch.leaky_relu(Tensor input,
double negative_slope,
boolean inplace) |
static Tensor |
torch.leaky_relu(Tensor input,
LeakyReLUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.leaky_relu
/** about the exact behavior of this functional.
|
static Tensor |
torch.leaky_relu(Tensor self,
Scalar negative_slope) |
static Tensor |
torch.lerp_out(Tensor out,
Tensor self,
Tensor end,
Scalar weight) |
static Tensor |
torch.lerp_out(Tensor out,
Tensor self,
Tensor end,
Tensor weight) |
static Tensor |
torch.lerp_outf(Tensor self,
Tensor end,
Scalar weight,
Tensor out) |
static Tensor |
torch.lerp_outf(Tensor self,
Tensor end,
Tensor weight,
Tensor out) |
static Tensor |
torch.lerp(Tensor self,
Tensor end,
Scalar weight) |
static Tensor |
torch.lerp(Tensor self,
Tensor end,
Tensor weight) |
static Tensor |
torch.less_equal_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.less_equal_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.less_equal_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.less_equal_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.less_equal(Tensor self,
Scalar other) |
static Tensor |
torch.less_equal(Tensor self,
Tensor other) |
static Tensor |
torch.less_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.less_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.less_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.less_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.less(Tensor self,
Scalar other) |
static Tensor |
torch.less(Tensor self,
Tensor other) |
static Tensor |
torch.lessThan(Scalar x,
Tensor y) |
static Tensor |
torch.lessThan(Tensor x,
Scalar y) |
static Tensor |
torch.lessThan(Tensor x,
Tensor y) |
static Tensor |
torch.lessThanEquals(Scalar x,
Tensor y) |
static Tensor |
torch.lessThanEquals(Tensor x,
Scalar y) |
static Tensor |
torch.lessThanEquals(Tensor x,
Tensor y) |
static Tensor |
torch.lgamma_out(Tensor out,
Tensor self) |
static Tensor |
torch.lgamma_outf(Tensor self,
Tensor out) |
static Tensor |
torch.lgamma(Tensor self) |
static Tensor |
torch.linalg_cholesky_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_cholesky_out(Tensor out,
Tensor self,
boolean upper) |
static Tensor |
torch.linalg_cholesky_outf(Tensor self,
boolean upper,
Tensor out) |
static Tensor |
torch.linalg_cholesky(Tensor self) |
static Tensor |
torch.linalg_cholesky(Tensor self,
boolean upper) |
static Tensor |
torch.linalg_cond_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_cond_out(Tensor out,
Tensor self,
Pointer p) |
static Tensor |
torch.linalg_cond_out(Tensor out,
Tensor self,
ScalarOptional p) |
static Tensor |
torch.linalg_cond_outf(Tensor self,
Pointer p,
Tensor out) |
static Tensor |
torch.linalg_cond_outf(Tensor self,
ScalarOptional p,
Tensor out) |
static Tensor |
torch.linalg_cond(Tensor self) |
static Tensor |
torch.linalg_cond(Tensor self,
Pointer p) |
static Tensor |
torch.linalg_cond(Tensor self,
ScalarOptional p) |
static Tensor |
torch.linalg_det_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_det_outf(Tensor self,
Tensor out) |
static Tensor |
torch.linalg_det(Tensor self) |
static Tensor |
torch.linalg_eigvals_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_eigvals_outf(Tensor self,
Tensor out) |
static Tensor |
torch.linalg_eigvals(Tensor self) |
static Tensor |
torch.linalg_eigvalsh_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_eigvalsh_out(Tensor out,
Tensor self,
Pointer UPLO) |
static Tensor |
torch.linalg_eigvalsh_outf(Tensor self,
Pointer UPLO,
Tensor out) |
static Tensor |
torch.linalg_eigvalsh(Tensor self) |
static Tensor |
torch.linalg_eigvalsh(Tensor self,
Pointer UPLO) |
static Tensor |
torch.linalg_householder_product_out(Tensor out,
Tensor input,
Tensor tau) |
static Tensor |
torch.linalg_householder_product_outf(Tensor input,
Tensor tau,
Tensor out) |
static Tensor |
torch.linalg_householder_product(Tensor input,
Tensor tau) |
static Tensor |
torch.linalg_inv_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_inv_outf(Tensor self,
Tensor out) |
static Tensor |
torch.linalg_inv(Tensor self) |
static Tensor |
torch.linalg_matmul_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.linalg_matmul_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.linalg_matmul(Tensor self,
Tensor other) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self,
Pointer ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self,
Pointer ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self,
Scalar ord) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self,
Scalar ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self,
Scalar ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm_outf(Tensor self,
Pointer ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_matrix_norm_outf(Tensor self,
Pointer ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_matrix_norm_outf(Tensor self,
Scalar ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_matrix_norm_outf(Tensor self,
Scalar ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_matrix_norm(Tensor self) |
static Tensor |
torch.linalg_matrix_norm(Tensor self,
Pointer ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm(Tensor self,
Pointer ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm(Tensor self,
Scalar ord) |
static Tensor |
torch.linalg_matrix_norm(Tensor self,
Scalar ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm(Tensor self,
Scalar ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_power_out(Tensor out,
Tensor self,
long n) |
static Tensor |
torch.linalg_matrix_power_outf(Tensor self,
long n,
Tensor out) |
static Tensor |
torch.linalg_matrix_power(Tensor self,
long n) |
static Tensor |
torch.linalg_matrix_rank_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_matrix_rank_out(Tensor out,
Tensor self,
DoubleOptional tol,
boolean hermitian) |
static Tensor |
torch.linalg_matrix_rank_out(Tensor out,
Tensor input,
Tensor tol) |
static Tensor |
torch.linalg_matrix_rank_out(Tensor out,
Tensor input,
Tensor tol,
boolean hermitian) |
static Tensor |
torch.linalg_matrix_rank_outf(Tensor self,
DoubleOptional tol,
boolean hermitian,
Tensor out) |
static Tensor |
torch.linalg_matrix_rank_outf(Tensor input,
Tensor tol,
boolean hermitian,
Tensor out) |
static Tensor |
torch.linalg_matrix_rank(Tensor self) |
static Tensor |
torch.linalg_matrix_rank(Tensor self,
DoubleOptional tol,
boolean hermitian) |
static Tensor |
torch.linalg_matrix_rank(Tensor input,
Tensor tol) |
static Tensor |
torch.linalg_matrix_rank(Tensor input,
Tensor tol,
boolean hermitian) |
static Tensor |
torch.linalg_multi_dot_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.linalg_multi_dot_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.linalg_multi_dot(TensorArrayRef tensors) |
static Tensor |
torch.linalg_norm_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_norm_out(Tensor out,
Tensor self,
Pointer ord) |
static Tensor |
torch.linalg_norm_out(Tensor out,
Tensor self,
Pointer ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_norm_out(Tensor out,
Tensor self,
ScalarOptional ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_norm_outf(Tensor self,
Pointer ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_norm_outf(Tensor self,
ScalarOptional ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_norm(Tensor self) |
static Tensor |
torch.linalg_norm(Tensor self,
Pointer ord) |
static Tensor |
torch.linalg_norm(Tensor self,
Pointer ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_norm(Tensor self,
ScalarOptional ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_pinv_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_pinv_out(Tensor out,
Tensor self,
double rcond,
boolean hermitian) |
static Tensor |
torch.linalg_pinv_out(Tensor out,
Tensor self,
Tensor rcond) |
static Tensor |
torch.linalg_pinv_out(Tensor out,
Tensor self,
Tensor rcond,
boolean hermitian) |
static Tensor |
torch.linalg_pinv_outf(Tensor self,
double rcond,
boolean hermitian,
Tensor out) |
static Tensor |
torch.linalg_pinv_outf(Tensor self,
Tensor rcond,
boolean hermitian,
Tensor out) |
static Tensor |
torch.linalg_pinv(Tensor self) |
static Tensor |
torch.linalg_pinv(Tensor self,
double rcond,
boolean hermitian) |
static Tensor |
torch.linalg_pinv(Tensor self,
Tensor rcond) |
static Tensor |
torch.linalg_pinv(Tensor self,
Tensor rcond,
boolean hermitian) |
static Tensor |
torch.linalg_solve_out(Tensor out,
Tensor input,
Tensor other) |
static Tensor |
torch.linalg_solve_outf(Tensor input,
Tensor other,
Tensor out) |
static Tensor |
torch.linalg_solve(Tensor input,
Tensor other) |
static Tensor |
torch.linalg_svdvals_out(Tensor out,
Tensor input) |
static Tensor |
torch.linalg_svdvals_outf(Tensor input,
Tensor out) |
static Tensor |
torch.linalg_svdvals(Tensor input) |
static Tensor |
torch.linalg_tensorinv_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_tensorinv_out(Tensor out,
Tensor self,
long ind) |
static Tensor |
torch.linalg_tensorinv_outf(Tensor self,
long ind,
Tensor out) |
static Tensor |
torch.linalg_tensorinv(Tensor self) |
static Tensor |
torch.linalg_tensorinv(Tensor self,
long ind) |
static Tensor |
torch.linalg_tensorsolve_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.linalg_tensorsolve_out(Tensor out,
Tensor self,
Tensor other,
LongArrayRefOptional dims) |
static Tensor |
torch.linalg_tensorsolve_outf(Tensor self,
Tensor other,
LongArrayRefOptional dims,
Tensor out) |
static Tensor |
torch.linalg_tensorsolve(Tensor self,
Tensor other) |
static Tensor |
torch.linalg_tensorsolve(Tensor self,
Tensor other,
LongArrayRefOptional dims) |
static Tensor |
torch.linalg_vector_norm_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_vector_norm_out(Tensor out,
Tensor self,
Scalar ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_vector_norm_outf(Tensor self,
Scalar ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_vector_norm(Tensor self) |
static Tensor |
torch.linalg_vector_norm(Tensor self,
Scalar ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linear_out(Tensor out,
Tensor input,
Tensor weight) |
static Tensor |
torch.linear_out(Tensor out,
Tensor input,
Tensor weight,
TensorOptional bias) |
static Tensor |
torch.linear_outf(Tensor input,
Tensor weight,
TensorOptional bias,
Tensor out) |
static Tensor |
torch.linear(Tensor input,
Tensor weight) |
static Tensor |
torch.linear(Tensor input,
Tensor weight,
Tensor bias) |
static Tensor |
torch.linear(Tensor input,
Tensor weight,
TensorOptional bias) |
static Tensor |
torch.linspace_out(Tensor out,
Scalar start,
Scalar end) |
static Tensor |
torch.linspace_out(Tensor out,
Scalar start,
Scalar end,
LongOptional steps) |
static Tensor |
torch.linspace_outf(Scalar start,
Scalar end,
LongOptional steps,
Tensor out) |
static Tensor |
torch.linspace(Scalar start,
Scalar end) |
static Tensor |
torch.linspace(Scalar start,
Scalar end,
LongOptional steps,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.linspace(Scalar start,
Scalar end,
LongOptional steps,
TensorOptions options) |
static Tensor |
torch.local_response_norm(Tensor input,
LocalResponseNormOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.local_response_norm
/** about the exact behavior of this functional.
|
static Tensor |
torch.local_response_norm(Tensor input,
long size,
double alpha,
double beta,
double k) |
static Tensor |
torch.log_(Tensor self) |
static Tensor |
torch.log_out(Tensor out,
Tensor self) |
static Tensor |
torch.log_outf(Tensor self,
Tensor out) |
static Tensor |
torch.log_sigmoid_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor buffer) |
static Tensor |
torch.log_sigmoid_backward_outf(Tensor grad_output,
Tensor self,
Tensor buffer,
Tensor grad_input) |
static Tensor |
torch.log_sigmoid_backward(Tensor grad_output,
Tensor self,
Tensor buffer) |
static Tensor |
torch.log_sigmoid_out(Tensor out,
Tensor self) |
static Tensor |
torch.log_sigmoid_outf(Tensor self,
Tensor out) |
static Tensor |
torch.log_sigmoid(Tensor self) |
static Tensor |
torch.log_softmax(Tensor self,
Dimname dim) |
static Tensor |
torch.log_softmax(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.log_softmax(Tensor input,
LogSoftmaxFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.log_softmax
/** about the exact behavior of this functional.
|
static Tensor |
torch.log_softmax(Tensor self,
long dim) |
static Tensor |
torch.log_softmax(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.log(Tensor self) |
static Tensor |
torch.log10_(Tensor self) |
static Tensor |
torch.log10_out(Tensor out,
Tensor self) |
static Tensor |
torch.log10_outf(Tensor self,
Tensor out) |
static Tensor |
torch.log10(Tensor self) |
static Tensor |
torch.log1p_(Tensor self) |
static Tensor |
torch.log1p_out(Tensor out,
Tensor self) |
static Tensor |
torch.log1p_outf(Tensor self,
Tensor out) |
static Tensor |
torch.log1p(Tensor self) |
static Tensor |
torch.log2_(Tensor self) |
static Tensor |
torch.log2_out(Tensor out,
Tensor self) |
static Tensor |
torch.log2_outf(Tensor self,
Tensor out) |
static Tensor |
torch.log2(Tensor self) |
static Tensor |
torch.logaddexp_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.logaddexp_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.logaddexp(Tensor self,
Tensor other) |
static Tensor |
torch.logaddexp2_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.logaddexp2_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.logaddexp2(Tensor self,
Tensor other) |
static Tensor |
torch.logcumsumexp_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.logcumsumexp_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.logcumsumexp_outf(Tensor self,
Dimname dim,
Tensor out) |
static Tensor |
torch.logcumsumexp_outf(Tensor self,
long dim,
Tensor out) |
static Tensor |
torch.logcumsumexp(Tensor self,
Dimname dim) |
static Tensor |
torch.logcumsumexp(Tensor self,
long dim) |
static Tensor |
torch.logdet(Tensor self) |
static Tensor |
torch.logical_and_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.logical_and_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.logical_and(Tensor self,
Tensor other) |
static Tensor |
torch.logical_not_out(Tensor out,
Tensor self) |
static Tensor |
torch.logical_not_outf(Tensor self,
Tensor out) |
static Tensor |
torch.logical_not(Tensor self) |
static Tensor |
torch.logical_or_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.logical_or_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.logical_or(Tensor self,
Tensor other) |
static Tensor |
torch.logical_xor_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.logical_xor_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.logical_xor(Tensor self,
Tensor other) |
static Tensor |
torch.logit_(Tensor self) |
static Tensor |
torch.logit_(Tensor self,
DoubleOptional eps) |
static Tensor |
torch.logit_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self) |
static Tensor |
torch.logit_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
DoubleOptional eps) |
static Tensor |
torch.logit_backward_outf(Tensor grad_output,
Tensor self,
DoubleOptional eps,
Tensor grad_input) |
static Tensor |
torch.logit_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.logit_backward(Tensor grad_output,
Tensor self,
DoubleOptional eps) |
static Tensor |
torch.logit_out(Tensor out,
Tensor self) |
static Tensor |
torch.logit_out(Tensor out,
Tensor self,
DoubleOptional eps) |
static Tensor |
torch.logit_outf(Tensor self,
DoubleOptional eps,
Tensor out) |
static Tensor |
torch.logit(Tensor self) |
static Tensor |
torch.logit(Tensor self,
DoubleOptional eps) |
static Tensor |
torch.logsigmoid(Tensor input) |
static Tensor |
torch.logspace_out(Tensor out,
Scalar start,
Scalar end) |
static Tensor |
torch.logspace_out(Tensor out,
Scalar start,
Scalar end,
LongOptional steps,
double base) |
static Tensor |
torch.logspace_outf(Scalar start,
Scalar end,
LongOptional steps,
double base,
Tensor out) |
static Tensor |
torch.logspace(Scalar start,
Scalar end) |
static Tensor |
torch.logspace(Scalar start,
Scalar end,
LongOptional steps,
double base,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.logspace(Scalar start,
Scalar end,
LongOptional steps,
double base,
TensorOptions options) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
boolean keepdim) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.logsumexp_outf(Tensor self,
DimnameArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.logsumexp_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.logsumexp_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.logsumexp(Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.logsumexp(Tensor self,
DimnameArrayRef dim,
boolean keepdim) |
static Tensor |
torch.logsumexp(Tensor self,
long... dim) |
static Tensor |
torch.logsumexp(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.logsumexp(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.logsumexp(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.lp_pool1d(Tensor input,
double norm_type,
LongPointer kernel_size,
LongPointer stride,
boolean ceil_mode) |
static Tensor |
torch.lp_pool1d(Tensor input,
LPPool1dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.lp_pool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.lp_pool2d(Tensor input,
double norm_type,
LongPointer kernel_size,
LongPointer stride,
boolean ceil_mode) |
static Tensor |
torch.lp_pool2d(Tensor input,
LPPool2dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.lp_pool2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.lt_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.lt_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.lt_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.lt_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.lt(Tensor self,
Scalar other) |
static Tensor |
torch.lt(Tensor self,
Tensor other) |
static Tensor |
torch.lu_solve_out(Tensor out,
Tensor self,
Tensor LU_data,
Tensor LU_pivots) |
static Tensor |
torch.lu_solve_outf(Tensor self,
Tensor LU_data,
Tensor LU_pivots,
Tensor out) |
static Tensor |
torch.lu_solve(Tensor self,
Tensor LU_data,
Tensor LU_pivots) |
static Tensor |
torch.make_variable_differentiable_view(Tensor data,
Pointer backward_info,
Pointer forward_info,
boolean shared_view_info,
byte creation_meta) |
static Tensor |
torch.make_variable_differentiable_view(Tensor data,
Pointer backward_info,
Pointer forward_info,
boolean shared_view_info,
byte creation_meta,
boolean allow_tensor_metadata_change) |
static Tensor |
torch.make_variable_differentiable_view(Tensor data,
Pointer backward_info,
Pointer forward_info,
boolean shared_view_info,
torch.CreationMeta creation_meta) |
static Tensor |
torch.make_variable_differentiable_view(Tensor data,
Pointer backward_info,
Pointer forward_info,
boolean shared_view_info,
torch.CreationMeta creation_meta,
boolean allow_tensor_metadata_change)
Creates a
Variable that is a *view* of another (*base*) variable. |
static Tensor |
torch.make_variable_non_differentiable_view(Tensor base,
Tensor data) |
static Tensor |
torch.make_variable_non_differentiable_view(Tensor base,
Tensor data,
boolean allow_tensor_metadata_change) |
static Tensor |
torch.make_variable(Tensor data) |
static Tensor |
torch.make_variable(Tensor data,
boolean requires_grad,
boolean allow_tensor_metadata_change)
Creates a
Variable from the given Tensor, copying its underlying TensorImpl. |
static Tensor |
torch.make_variable(Tensor data,
Edge gradient_edge) |
static Tensor |
torch.make_variable(Tensor data,
Edge gradient_edge,
boolean allow_tensor_metadata_change)
Creates a
Variable from the given Tensor, copying its underlying TensorImpl. |
static Tensor |
torch.margin_ranking_loss(Tensor input1,
Tensor input2,
Tensor target) |
static Tensor |
torch.margin_ranking_loss(Tensor input1,
Tensor input2,
Tensor target,
double margin,
long reduction) |
static Tensor |
torch.margin_ranking_loss(Tensor input1,
Tensor input2,
Tensor target,
double margin,
loss_reduction_t reduction) |
static Tensor |
torch.margin_ranking_loss(Tensor input1,
Tensor input2,
Tensor target,
MarginRankingLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.margin_ranking_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.masked_fill(Tensor self,
Tensor mask,
Scalar value) |
static Tensor |
torch.masked_fill(Tensor self,
Tensor mask,
Tensor value) |
static Tensor |
torch.masked_scatter(Tensor self,
Tensor mask,
Tensor source) |
static Tensor |
torch.masked_select_backward(Tensor grad,
Tensor input,
Tensor mask) |
static Tensor |
torch.masked_select_out(Tensor out,
Tensor self,
Tensor mask) |
static Tensor |
torch.masked_select_outf(Tensor self,
Tensor mask,
Tensor out) |
static Tensor |
torch.masked_select(Tensor self,
Tensor mask) |
static Tensor |
torch.matmul_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.matmul_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.matmul(Tensor self,
Tensor other) |
static Tensor |
torch.matrix_exp_backward(Tensor self,
Tensor grad) |
static Tensor |
torch.matrix_exp(Tensor self) |
static Tensor |
torch.matrix_power_out(Tensor out,
Tensor self,
long n) |
static Tensor |
torch.matrix_power_outf(Tensor self,
long n,
Tensor out) |
static Tensor |
torch.matrix_power(Tensor self,
long n) |
static Tensor |
torch.matrix_rank(Tensor self) |
static Tensor |
torch.matrix_rank(Tensor self,
boolean symmetric) |
static Tensor |
torch.matrix_rank(Tensor self,
double tol) |
static Tensor |
torch.matrix_rank(Tensor self,
double tol,
boolean symmetric) |
static Tensor |
torch.max_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.max_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.max_pool1d(Tensor self,
long... kernel_size) |
static Tensor |
torch.max_pool1d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool1d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.max_pool1d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool1d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool1d(Tensor input,
MaxPool1dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_pool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max_pool2d_with_indices_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool2d_with_indices_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool2d_with_indices_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.max_pool2d_with_indices_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.max_pool2d_with_indices_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool2d_with_indices_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool2d(Tensor self,
long... kernel_size) |
static Tensor |
torch.max_pool2d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool2d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.max_pool2d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool2d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool2d(Tensor input,
MaxPool2dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_pool2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max_pool3d_with_indices_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool3d_with_indices_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool3d_with_indices_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.max_pool3d_with_indices_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.max_pool3d_with_indices_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool3d_with_indices_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool3d(Tensor self,
long... kernel_size) |
static Tensor |
torch.max_pool3d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool3d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.max_pool3d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool3d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool3d(Tensor input,
MaxPool3dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_pool3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max_unpool1d(Tensor input,
Tensor indices,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongVectorOptional output_size) |
static Tensor |
torch.max_unpool1d(Tensor input,
Tensor indices,
MaxUnpool1dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_unpool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max_unpool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices,
long... output_size) |
static Tensor |
torch.max_unpool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size) |
static Tensor |
torch.max_unpool2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
long[] output_size,
Tensor grad_input) |
static Tensor |
torch.max_unpool2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size,
Tensor grad_input) |
static Tensor |
torch.max_unpool2d_backward(Tensor grad_output,
Tensor self,
Tensor indices,
long... output_size) |
static Tensor |
torch.max_unpool2d_backward(Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size) |
static Tensor |
torch.max_unpool2d_out(Tensor out,
Tensor self,
Tensor indices,
long... output_size) |
static Tensor |
torch.max_unpool2d_out(Tensor out,
Tensor self,
Tensor indices,
LongArrayRef output_size) |
static Tensor |
torch.max_unpool2d_outf(Tensor self,
Tensor indices,
long[] output_size,
Tensor out) |
static Tensor |
torch.max_unpool2d_outf(Tensor self,
Tensor indices,
LongArrayRef output_size,
Tensor out) |
static Tensor |
torch.max_unpool2d(Tensor self,
Tensor indices,
long... output_size) |
static Tensor |
torch.max_unpool2d(Tensor self,
Tensor indices,
LongArrayRef output_size) |
static Tensor |
torch.max_unpool2d(Tensor input,
Tensor indices,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongVectorOptional output_size) |
static Tensor |
torch.max_unpool2d(Tensor input,
Tensor indices,
MaxUnpool2dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_unpool2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max_unpool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long... padding) |
static Tensor |
torch.max_unpool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.max_unpool3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.max_unpool3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.max_unpool3d_backward(Tensor grad_output,
Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long... padding) |
static Tensor |
torch.max_unpool3d_backward(Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.max_unpool3d_out(Tensor out,
Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long... padding) |
static Tensor |
torch.max_unpool3d_out(Tensor out,
Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.max_unpool3d_outf(Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long[] padding,
Tensor out) |
static Tensor |
torch.max_unpool3d_outf(Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.max_unpool3d(Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long... padding) |
static Tensor |
torch.max_unpool3d(Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.max_unpool3d(Tensor input,
Tensor indices,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongVectorOptional output_size) |
static Tensor |
torch.max_unpool3d(Tensor input,
Tensor indices,
MaxUnpool3dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_unpool3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max(Tensor self) |
static Tensor |
torch.max(Tensor self,
Tensor other) |
static Tensor |
torch.maximum_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.maximum_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.maximum(Tensor self,
Tensor other) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean_outf(Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.mean_outf(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.mean_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.mean(Tensor self) |
static Tensor |
torch.mean(Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.mean(Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean(Tensor self,
long... dim) |
static Tensor |
torch.mean(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.mean(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean(Tensor self,
ScalarTypeOptional dtype) |
static Tensor |
torch.median(Tensor self) |
static Tensor |
torch.min_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.min_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.min(Tensor self) |
static Tensor |
torch.min(Tensor self,
Tensor other) |
static Tensor |
torch.minimum_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.minimum_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.minimum(Tensor self,
Tensor other) |
static Tensor |
torch.miopen_convolution_backward_bias(Tensor grad_output) |
static Tensor |
torch.miopen_convolution_backward_input(long[] self_size,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_backward_input(LongArrayRef self_size,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_backward_weight(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_backward_weight(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_transpose_backward_input(Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_transpose_backward_input(Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_transpose_backward_weight(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_transpose_backward_weight(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_transpose(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_transpose(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution_backward_input(long[] self_size,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution_backward_input(LongArrayRef self_size,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution_backward_weight(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution_backward_weight(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.mish_(Tensor self) |
static Tensor |
torch.mish_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.mish_out(Tensor out,
Tensor self) |
static Tensor |
torch.mish_outf(Tensor self,
Tensor out) |
static Tensor |
torch.mish(Tensor self) |
static Tensor |
torch.mkldnn_adaptive_avg_pool2d_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.mkldnn_adaptive_avg_pool2d(Tensor self,
long... output_size) |
static Tensor |
torch.mkldnn_adaptive_avg_pool2d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.mkldnn_convolution_backward_input(long[] self_size,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean bias_defined) |
static Tensor |
torch.mkldnn_convolution_backward_input(LongArrayRef self_size,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean bias_defined) |
static Tensor |
torch.mkldnn_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] stride,
long[] dilation,
long groups) |
static Tensor |
torch.mkldnn_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.mkldnn_linear_backward_input(long[] input_size,
Tensor grad_output,
Tensor weight) |
static Tensor |
torch.mkldnn_linear_backward_input(LongArrayRef input_size,
Tensor grad_output,
Tensor weight) |
static Tensor |
torch.mkldnn_linear(Tensor self,
Tensor weight) |
static Tensor |
torch.mkldnn_linear(Tensor self,
Tensor weight,
TensorOptional bias) |
static Tensor |
torch.mkldnn_max_pool2d_backward(Tensor grad_output,
Tensor output,
Tensor input,
long... kernel_size) |
static Tensor |
torch.mkldnn_max_pool2d_backward(Tensor grad_output,
Tensor output,
Tensor input,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool2d_backward(Tensor grad_output,
Tensor output,
Tensor input,
LongArrayRef kernel_size) |
static Tensor |
torch.mkldnn_max_pool2d_backward(Tensor grad_output,
Tensor output,
Tensor input,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool2d(Tensor self,
long... kernel_size) |
static Tensor |
torch.mkldnn_max_pool2d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool2d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.mkldnn_max_pool2d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool3d_backward(Tensor grad_output,
Tensor output,
Tensor input,
long... kernel_size) |
static Tensor |
torch.mkldnn_max_pool3d_backward(Tensor grad_output,
Tensor output,
Tensor input,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool3d_backward(Tensor grad_output,
Tensor output,
Tensor input,
LongArrayRef kernel_size) |
static Tensor |
torch.mkldnn_max_pool3d_backward(Tensor grad_output,
Tensor output,
Tensor input,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool3d(Tensor self,
long... kernel_size) |
static Tensor |
torch.mkldnn_max_pool3d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool3d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.mkldnn_max_pool3d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_reorder_conv2d_weight(Tensor self) |
static Tensor |
torch.mkldnn_reorder_conv2d_weight(Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups) |
static Tensor |
torch.mkldnn_reorder_conv2d_weight(Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.mkldnn_reorder_conv3d_weight(Tensor self) |
static Tensor |
torch.mkldnn_reorder_conv3d_weight(Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups) |
static Tensor |
torch.mkldnn_reorder_conv3d_weight(Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.mm_out(Tensor out,
Tensor self,
Tensor mat2) |
static Tensor |
torch.mm_outf(Tensor self,
Tensor mat2,
Tensor out) |
static Tensor |
torch.mm(Tensor self,
Tensor mat2) |
static Tensor |
torch.mod(Scalar x,
Tensor y) |
static Tensor |
torch.mod(Tensor x,
Scalar y) |
static Tensor |
torch.mod(Tensor x,
Tensor y) |
static Tensor |
torch.moveaxis(Tensor self,
long[] source,
long... destination) |
static Tensor |
torch.moveaxis(Tensor self,
LongArrayRef source,
LongArrayRef destination) |
static Tensor |
torch.moveaxis(Tensor self,
long source,
long destination) |
static Tensor |
torch.movedim(Tensor self,
long[] source,
long... destination) |
static Tensor |
torch.movedim(Tensor self,
LongArrayRef source,
LongArrayRef destination) |
static Tensor |
torch.movedim(Tensor self,
long source,
long destination) |
static Tensor |
torch.mse_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.mse_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor grad_input) |
static Tensor |
torch.mse_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.mse_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.mse_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.mse_loss_outf(Tensor self,
Tensor target,
long reduction,
Tensor out) |
static Tensor |
torch.mse_loss(Tensor self,
Tensor target) |
static Tensor |
torch.mse_loss(Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.mse_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.mse_loss(Tensor input,
Tensor target,
MSELossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.mse_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.msort_out(Tensor out,
Tensor self) |
static Tensor |
torch.msort_outf(Tensor self,
Tensor out) |
static Tensor |
torch.msort(Tensor self) |
static Tensor |
torch.mul_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.mul_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.mul(Tensor self,
Scalar other) |
static Tensor |
torch.mul(Tensor self,
Tensor other) |
static Tensor |
torch.multi_margin_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
Scalar p,
Scalar margin) |
static Tensor |
torch.multi_margin_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction) |
static Tensor |
torch.multi_margin_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction,
Tensor grad_input) |
static Tensor |
torch.multi_margin_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
Scalar p,
Scalar margin) |
static Tensor |
torch.multi_margin_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction) |
static Tensor |
torch.multi_margin_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.multi_margin_loss_out(Tensor out,
Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction) |
static Tensor |
torch.multi_margin_loss_outf(Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction,
Tensor out) |
static Tensor |
torch.multi_margin_loss(Tensor self,
Tensor target) |
static Tensor |
torch.multi_margin_loss(Tensor input,
Tensor target,
long p,
double margin,
Tensor weight,
loss_reduction_t reduction) |
static Tensor |
torch.multi_margin_loss(Tensor input,
Tensor target,
MultiMarginLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.multi_margin_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.multi_margin_loss(Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction) |
static Tensor |
torch.multilabel_margin_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor is_target) |
static Tensor |
torch.multilabel_margin_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor is_target,
Tensor grad_input) |
static Tensor |
torch.multilabel_margin_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor is_target) |
static Tensor |
torch.multilabel_margin_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.multilabel_margin_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.multilabel_margin_loss_outf(Tensor self,
Tensor target,
long reduction,
Tensor out) |
static Tensor |
torch.multilabel_margin_loss(Tensor self,
Tensor target) |
static Tensor |
torch.multilabel_margin_loss(Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.multilabel_margin_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.multilabel_margin_loss(Tensor input,
Tensor target,
MultiLabelMarginLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.multilabel_margin_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.multilabel_soft_margin_loss(Tensor input,
Tensor target) |
static Tensor |
torch.multilabel_soft_margin_loss(Tensor input,
Tensor target,
MultiLabelSoftMarginLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.multilabel_soft_margin_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.multilabel_soft_margin_loss(Tensor input,
Tensor target,
Tensor weight,
loss_reduction_t reduction) |
static Tensor |
torch.multinomial_out(Tensor out,
Tensor self,
long num_samples) |
static Tensor |
torch.multinomial_out(Tensor out,
Tensor self,
long num_samples,
boolean replacement,
GeneratorOptional generator) |
static Tensor |
torch.multinomial_outf(Tensor self,
long num_samples,
boolean replacement,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.multinomial(Tensor self,
long num_samples) |
static Tensor |
torch.multinomial(Tensor self,
long num_samples,
boolean replacement,
GeneratorOptional generator) |
static Tensor |
torch.multiply_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.multiply_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.multiply(Scalar x,
Tensor y) |
static Tensor |
torch.multiply(Tensor self,
Scalar other) |
static Tensor |
torch.multiply(Tensor self,
Tensor other) |
static Tensor |
torch.mv_out(Tensor out,
Tensor self,
Tensor vec) |
static Tensor |
torch.mv_outf(Tensor self,
Tensor vec,
Tensor out) |
static Tensor |
torch.mv(Tensor self,
Tensor vec) |
static Tensor |
torch.mvlgamma_out(Tensor out,
Tensor self,
long p) |
static Tensor |
torch.mvlgamma_outf(Tensor self,
long p,
Tensor out) |
static Tensor |
torch.mvlgamma(Tensor self,
long p) |
static Tensor |
torch.nan_to_num_(Tensor self) |
static Tensor |
torch.nan_to_num_(Tensor self,
DoubleOptional nan,
DoubleOptional posinf,
DoubleOptional neginf) |
static Tensor |
torch.nan_to_num_out(Tensor out,
Tensor self) |
static Tensor |
torch.nan_to_num_out(Tensor out,
Tensor self,
DoubleOptional nan,
DoubleOptional posinf,
DoubleOptional neginf) |
static Tensor |
torch.nan_to_num_outf(Tensor self,
DoubleOptional nan,
DoubleOptional posinf,
DoubleOptional neginf,
Tensor out) |
static Tensor |
torch.nan_to_num(Tensor self) |
static Tensor |
torch.nan_to_num(Tensor self,
DoubleOptional nan,
DoubleOptional posinf,
DoubleOptional neginf) |
static Tensor |
torch.nanmean_out(Tensor out,
Tensor self) |
static Tensor |
torch.nanmean_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nanmean_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nanmean_outf(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.nanmean_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.nanmean(Tensor self) |
static Tensor |
torch.nanmean(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nanmean(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nanmedian(Tensor self) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
double q) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
double q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
Tensor q) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.nanquantile_outf(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation,
Tensor out) |
static Tensor |
torch.nanquantile_outf(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.nanquantile_outf(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation,
Tensor out) |
static Tensor |
torch.nanquantile_outf(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.nanquantile(Tensor self,
double q) |
static Tensor |
torch.nanquantile(Tensor self,
double q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.nanquantile(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.nanquantile(Tensor self,
Tensor q) |
static Tensor |
torch.nanquantile(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.nanquantile(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.nansum_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.nansum_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nansum_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.nansum_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nansum_outf(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.nansum_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.nansum(Tensor self) |
static Tensor |
torch.nansum(Tensor self,
long... dim) |
static Tensor |
torch.nansum(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nansum(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.nansum(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nansum(Tensor self,
ScalarTypeOptional dtype) |
static Tensor |
torch.narrow_copy_out(Tensor out,
Tensor self,
long dim,
long start,
long length) |
static Tensor |
torch.narrow_copy_outf(Tensor self,
long dim,
long start,
long length,
Tensor out) |
static Tensor |
torch.narrow_copy(Tensor self,
long dim,
long start,
long length) |
static Tensor |
torch.narrow(Tensor self,
long dim,
long start,
long length) |
static Tensor |
torch.narrow(Tensor self,
long dim,
Tensor start,
long length) |
static Tensor |
torch.native_norm(Tensor self) |
static Tensor |
torch.native_norm(Tensor self,
Scalar p) |
static Tensor |
torch.native_norm(Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.native_norm(Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.ne_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.ne_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.ne_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.ne_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.ne(Tensor self,
Scalar other) |
static Tensor |
torch.ne(Tensor self,
Tensor other) |
static Tensor |
torch.neg_(Tensor self) |
static Tensor |
torch.neg_out(Tensor out,
Tensor self) |
static Tensor |
torch.neg_outf(Tensor self,
Tensor out) |
static Tensor |
torch.neg(Tensor self) |
static Tensor |
torch.negative_(Tensor self) |
static Tensor |
torch.negative_out(Tensor out,
Tensor self) |
static Tensor |
torch.negative_outf(Tensor self,
Tensor out) |
static Tensor |
torch.negative(Tensor self) |
static Tensor |
torch.nextafter_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.nextafter_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.nextafter(Tensor self,
Tensor other) |
static Tensor |
torch.nll_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight) |
static Tensor |
torch.nll_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight,
Tensor grad_input) |
static Tensor |
torch.nll_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight) |
static Tensor |
torch.nll_loss_nd(Tensor self,
Tensor target) |
static Tensor |
torch.nll_loss_nd(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nll_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.nll_loss_out(Tensor out,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nll_loss_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor out) |
static Tensor |
torch.nll_loss(Tensor self,
Tensor target) |
static Tensor |
torch.nll_loss(Tensor input,
Tensor target,
NLLLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.nll_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.nll_loss(Tensor input,
Tensor target,
Tensor weight,
long ignore_index,
loss_reduction_t reduction) |
static Tensor |
torch.nll_loss(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nll_loss2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight) |
static Tensor |
torch.nll_loss2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight,
Tensor grad_input) |
static Tensor |
torch.nll_loss2d_backward(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight) |
static Tensor |
torch.nll_loss2d_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.nll_loss2d_out(Tensor out,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nll_loss2d_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor out) |
static Tensor |
torch.nll_loss2d(Tensor self,
Tensor target) |
static Tensor |
torch.nll_loss2d(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nonzero_out(Tensor out,
Tensor self) |
static Tensor |
torch.nonzero_outf(Tensor self,
Tensor out) |
static Tensor |
torch.nonzero(Tensor self) |
static Tensor |
torch.norm_except_dim(Tensor v) |
static Tensor |
torch.norm_except_dim(Tensor v,
long pow,
long dim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
DimnameArrayRef dim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
long... dim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
LongArrayRef dim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim,
torch.ScalarType dtype,
Tensor out) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim,
torch.ScalarType dtype,
Tensor out) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
torch.ScalarType dtype,
Tensor out) |
static Tensor |
torch.norm(Tensor self) |
static Tensor |
torch.norm(Tensor self,
Scalar p) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
DimnameArrayRef dim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
long... dim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
LongArrayRef dim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
torch.ScalarType dtype) |
static Tensor |
torch.normal_(Tensor tensor) |
static Tensor |
torch.normal_(Tensor tensor,
double mean,
double std)
Fills the given 2-dimensional
matrix with values drawn from a normal
distribution parameterized by mean and std. |
static Tensor |
torch.normal_out(Tensor out,
double mean,
double std,
long... size) |
static Tensor |
torch.normal_out(Tensor out,
double mean,
double std,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.normal_out(Tensor out,
double mean,
double std,
LongArrayRef size) |
static Tensor |
torch.normal_out(Tensor out,
double mean,
double std,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.normal_out(Tensor out,
double mean,
Tensor std) |
static Tensor |
torch.normal_out(Tensor out,
double mean,
Tensor std,
GeneratorOptional generator) |
static Tensor |
torch.normal_out(Tensor out,
Tensor mean) |
static Tensor |
torch.normal_out(Tensor out,
Tensor mean,
double std,
GeneratorOptional generator) |
static Tensor |
torch.normal_out(Tensor out,
Tensor mean,
Tensor std) |
static Tensor |
torch.normal_out(Tensor out,
Tensor mean,
Tensor std,
GeneratorOptional generator) |
static Tensor |
torch.normal_outf(double mean,
double std,
long[] size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.normal_outf(double mean,
double std,
LongArrayRef size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.normal_outf(double mean,
Tensor std,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.normal_outf(Tensor mean,
double std,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.normal_outf(Tensor mean,
Tensor std,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.normal(double mean,
double std,
long... size) |
static Tensor |
torch.normal(double mean,
double std,
long[] size,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.normal(double mean,
double std,
long[] size,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.normal(double mean,
double std,
LongArrayRef size) |
static Tensor |
torch.normal(double mean,
double std,
LongArrayRef size,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.normal(double mean,
double std,
LongArrayRef size,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.normal(double mean,
Tensor std) |
static Tensor |
torch.normal(double mean,
Tensor std,
GeneratorOptional generator) |
static Tensor |
torch.normal(Tensor mean) |
static Tensor |
torch.normal(Tensor mean,
double std,
GeneratorOptional generator) |
static Tensor |
torch.normal(Tensor mean,
Tensor std) |
static Tensor |
torch.normal(Tensor mean,
Tensor std,
GeneratorOptional generator) |
static Tensor |
torch.normalize(Tensor input) |
static Tensor |
torch.normalize(Tensor input,
double p,
long dim,
double eps,
TensorOptional out) |
static Tensor |
torch.normalize(Tensor input,
NormalizeFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.normalize
/** about the exact behavior of this functional.
|
static Tensor |
torch.not_equal_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.not_equal_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.not_equal_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.not_equal_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.not_equal(Tensor self,
Scalar other) |
static Tensor |
torch.not_equal(Tensor self,
Tensor other) |
static Tensor |
torch.notEquals(Scalar x,
Tensor y) |
static Tensor |
torch.notEquals(Tensor x,
Scalar y) |
static Tensor |
torch.notEquals(Tensor x,
Tensor y) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self,
boolean keepdim) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.nuclear_norm_outf(Tensor self,
boolean keepdim,
Tensor out) |
static Tensor |
torch.nuclear_norm_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.nuclear_norm_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.nuclear_norm(Tensor self) |
static Tensor |
torch.nuclear_norm(Tensor self,
boolean keepdim) |
static Tensor |
torch.nuclear_norm(Tensor self,
long... dim) |
static Tensor |
torch.nuclear_norm(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.nuclear_norm(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.nuclear_norm(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.one_hot(Tensor self) |
static Tensor |
torch.one_hot(Tensor self,
long num_classes) |
static Tensor |
torch.ones_(Tensor tensor)
Fills the given
tensor with ones. |
static Tensor |
torch.ones_like(Tensor self) |
static Tensor |
torch.ones_like(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.ones_like(Tensor self,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.ones_out(Tensor out,
long... size) |
static Tensor |
torch.ones_out(Tensor out,
LongArrayRef size) |
static Tensor |
torch.ones_outf(long[] size,
Tensor out) |
static Tensor |
torch.ones_outf(LongArrayRef size,
Tensor out) |
static Tensor |
torch.ones(long... size) |
static Tensor |
torch.ones(long[] size,
DimnameListOptional names) |
static Tensor |
torch.ones(long[] size,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.ones(long[] size,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.ones(long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.ones(long[] size,
TensorOptions options) |
static Tensor |
torch.ones(LongArrayRef size) |
static Tensor |
torch.ones(LongArrayRef size,
DimnameListOptional names) |
static Tensor |
torch.ones(LongArrayRef size,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.ones(LongArrayRef size,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.ones(LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.ones(LongArrayRef size,
TensorOptions options) |
static Tensor |
torch.or(Scalar x,
Tensor y) |
static Tensor |
torch.or(Tensor x,
Scalar y) |
static Tensor |
torch.or(Tensor x,
Tensor y) |
static Tensor |
torch.orgqr_out(Tensor out,
Tensor self,
Tensor input2) |
static Tensor |
torch.orgqr_outf(Tensor self,
Tensor input2,
Tensor out) |
static Tensor |
torch.orgqr(Tensor self,
Tensor input2) |
static Tensor |
torch.ormqr_out(Tensor out,
Tensor self,
Tensor input2,
Tensor input3) |
static Tensor |
torch.ormqr_out(Tensor out,
Tensor self,
Tensor input2,
Tensor input3,
boolean left,
boolean transpose) |
static Tensor |
torch.ormqr_outf(Tensor self,
Tensor input2,
Tensor input3,
boolean left,
boolean transpose,
Tensor out) |
static Tensor |
torch.ormqr(Tensor self,
Tensor input2,
Tensor input3) |
static Tensor |
torch.ormqr(Tensor self,
Tensor input2,
Tensor input3,
boolean left,
boolean transpose) |
static Tensor |
torch.orthogonal_(Tensor tensor) |
static Tensor |
torch.orthogonal_(Tensor tensor,
double gain)
Fills the input
Tensor with a (semi) orthogonal matrix, as described in
"Exact solutions to the nonlinear dynamics of learning in deep linear neural
networks" - Saxe, A. |
static Tensor |
torch.outer_out(Tensor out,
Tensor self,
Tensor vec2) |
static Tensor |
torch.outer_outf(Tensor self,
Tensor vec2,
Tensor out) |
static Tensor |
torch.outer(Tensor self,
Tensor vec2) |
static Tensor |
torch.pad_sequence(TensorArrayRef sequences) |
static Tensor |
torch.pad_sequence(TensorArrayRef sequences,
boolean batch_first,
double padding_value) |
static Tensor |
torch.pad(Tensor input,
long[] pad,
pad_mode_t mode,
double value) |
static Tensor |
torch.pad(Tensor input,
LongArrayRef pad,
pad_mode_t mode,
double value) |
static Tensor |
torch.pad(Tensor input,
PadFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.pad
/** about the exact behavior of this functional.
|
static Tensor |
torch.pairwise_distance(Tensor x1,
Tensor x2) |
static Tensor |
torch.pairwise_distance(Tensor x1,
Tensor x2,
double p,
double eps,
boolean keepdim) |
static Tensor |
torch.pairwise_distance(Tensor x1,
Tensor x2,
PairwiseDistanceOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.pairwise_distance
/** about the exact behavior of this functional.
|
static Tensor |
torch.parameters_to_vector(Tensor parameters) |
static Tensor |
torch.pdist(Tensor self) |
static Tensor |
torch.pdist(Tensor self,
double p) |
static Tensor |
torch.permute(Tensor self,
long... dims) |
static Tensor |
torch.permute(Tensor self,
LongArrayRef dims) |
static Tensor |
torch.pinverse(Tensor self) |
static Tensor |
torch.pinverse(Tensor self,
double rcond) |
static Tensor |
torch.pixel_shuffle(Tensor self,
long upscale_factor) |
static Tensor |
torch.pixel_shuffle(Tensor input,
PixelShuffleOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.pixel_shuffle
/** about the exact behavior of this functional.
|
static Tensor |
torch.pixel_unshuffle(Tensor self,
long downscale_factor) |
static Tensor |
torch.pixel_unshuffle(Tensor input,
PixelUnshuffleOptions options) |
static Tensor |
torch.poisson_nll_loss(Tensor input,
Tensor target) |
static Tensor |
torch.poisson_nll_loss(Tensor input,
Tensor target,
boolean log_input,
boolean full,
double eps,
long reduction) |
static Tensor |
torch.poisson_nll_loss(Tensor input,
Tensor target,
boolean log_input,
boolean full,
double eps,
loss_reduction_t reduction) |
static Tensor |
torch.poisson_nll_loss(Tensor input,
Tensor target,
PoissonNLLLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.poisson_nll_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.poisson(Tensor self) |
static Tensor |
torch.poisson(Tensor self,
GeneratorOptional generator) |
static Tensor |
torch.polar_out(Tensor out,
Tensor abs,
Tensor angle) |
static Tensor |
torch.polar_outf(Tensor abs,
Tensor angle,
Tensor out) |
static Tensor |
torch.polar(Tensor abs,
Tensor angle) |
static Tensor |
torch.polygamma_out(Tensor out,
long n,
Tensor self) |
static Tensor |
torch.polygamma_outf(long n,
Tensor self,
Tensor out) |
static Tensor |
torch.polygamma(long n,
Tensor self) |
static Tensor |
torch.positive(Tensor self) |
static Tensor |
torch.pow_out(Tensor out,
Scalar self,
Tensor exponent) |
static Tensor |
torch.pow_out(Tensor out,
Tensor self,
Scalar exponent) |
static Tensor |
torch.pow_out(Tensor out,
Tensor self,
Tensor exponent) |
static Tensor |
torch.pow_outf(Scalar self,
Tensor exponent,
Tensor out) |
static Tensor |
torch.pow_outf(Tensor self,
Scalar exponent,
Tensor out) |
static Tensor |
torch.pow_outf(Tensor self,
Tensor exponent,
Tensor out) |
static Tensor |
torch.pow(Scalar self,
Tensor exponent) |
static Tensor |
torch.pow(Tensor self,
Scalar exponent) |
static Tensor |
torch.pow(Tensor self,
Tensor exponent) |
static Tensor |
torch.prelu(Tensor self,
Tensor weight) |
static Tensor |
torch.prod_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.prod_out(Tensor out,
Tensor self,
Dimname dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.prod_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.prod_out(Tensor out,
Tensor self,
long dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.prod_outf(Tensor self,
Dimname dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.prod_outf(Tensor self,
long dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.prod(Tensor self) |
static Tensor |
torch.prod(Tensor self,
Dimname dim) |
static Tensor |
torch.prod(Tensor self,
Dimname dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.prod(Tensor self,
long dim) |
static Tensor |
torch.prod(Tensor self,
long dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.prod(Tensor self,
ScalarTypeOptional dtype) |
static Tensor |
torch.propagate_names_if_nonempty(Tensor result,
DimnameArrayRef maybe_names) |
static Tensor |
torch.propagate_names_if_nonempty(Tensor result,
DimnameArrayRef maybe_names,
boolean validate_names) |
static Tensor |
torch.propagate_names(Tensor result,
DimnameArrayRef names) |
static Tensor |
torch.propagate_names(Tensor result,
DimnameArrayRef names,
boolean validate_names) |
static Tensor |
torch.put(Tensor self,
Tensor index,
Tensor source) |
static Tensor |
torch.put(Tensor self,
Tensor index,
Tensor source,
boolean accumulate) |
static Tensor |
torch.q_per_channel_scales(Tensor self) |
static Tensor |
torch.q_per_channel_zero_points(Tensor self) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
double q) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
double q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
Tensor q) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.quantile_outf(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation,
Tensor out) |
static Tensor |
torch.quantile_outf(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.quantile_outf(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation,
Tensor out) |
static Tensor |
torch.quantile_outf(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.quantile(Tensor self,
double q) |
static Tensor |
torch.quantile(Tensor self,
double q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.quantile(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.quantile(Tensor self,
Tensor q) |
static Tensor |
torch.quantile(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.quantile(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.quantize_per_channel(Tensor self,
Tensor scales,
Tensor zero_points,
long axis,
torch.ScalarType dtype) |
static Tensor |
torch.quantize_per_tensor(Tensor self,
double scale,
long zero_point,
torch.ScalarType dtype) |
static Tensor |
torch.quantize_per_tensor(Tensor self,
Tensor scale,
Tensor zero_point,
torch.ScalarType dtype) |
static Tensor |
torch.quantized_batch_norm(Tensor input,
TensorOptional weight,
TensorOptional bias,
Tensor mean,
Tensor var,
double eps,
double output_scale,
long output_zero_point) |
static Tensor |
torch.quantized_gru_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
Tensor b_ih,
Tensor b_hh,
Tensor packed_ih,
Tensor packed_hh,
Tensor col_offsets_ih,
Tensor col_offsets_hh,
Scalar scale_ih,
Scalar scale_hh,
Scalar zero_point_ih,
Scalar zero_point_hh) |
static Tensor |
torch.quantized_max_pool1d(Tensor self,
long... kernel_size) |
static Tensor |
torch.quantized_max_pool1d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.quantized_max_pool1d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.quantized_max_pool1d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.quantized_max_pool2d(Tensor self,
long... kernel_size) |
static Tensor |
torch.quantized_max_pool2d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.quantized_max_pool2d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.quantized_max_pool2d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.quantized_rnn_relu_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
Tensor b_ih,
Tensor b_hh,
Tensor packed_ih,
Tensor packed_hh,
Tensor col_offsets_ih,
Tensor col_offsets_hh,
Scalar scale_ih,
Scalar scale_hh,
Scalar zero_point_ih,
Scalar zero_point_hh) |
static Tensor |
torch.quantized_rnn_tanh_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
Tensor b_ih,
Tensor b_hh,
Tensor packed_ih,
Tensor packed_hh,
Tensor col_offsets_ih,
Tensor col_offsets_hh,
Scalar scale_ih,
Scalar scale_hh,
Scalar zero_point_ih,
Scalar zero_point_hh) |
static Tensor |
torch.rad2deg_(Tensor self) |
static Tensor |
torch.rad2deg_out(Tensor out,
Tensor self) |
static Tensor |
torch.rad2deg_outf(Tensor self,
Tensor out) |
static Tensor |
torch.rad2deg(Tensor self) |
static Tensor |
torch.rand_like(Tensor self) |
static Tensor |
torch.rand_like(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.rand_like(Tensor self,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.rand_out(Tensor out,
long... size) |
static Tensor |
torch.rand_out(Tensor out,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.rand_out(Tensor out,
LongArrayRef size) |
static Tensor |
torch.rand_out(Tensor out,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.rand_outf(long[] size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.rand_outf(long[] size,
Tensor out) |
static Tensor |
torch.rand_outf(LongArrayRef size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.rand_outf(LongArrayRef size,
Tensor out) |
static Tensor |
torch.rand(long... size) |
static Tensor |
torch.rand(long[] size,
DimnameListOptional names) |
static Tensor |
torch.rand(long[] size,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.rand(long[] size,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.rand(long[] size,
GeneratorOptional generator) |
static Tensor |
torch.rand(long[] size,
GeneratorOptional generator,
DimnameListOptional names) |
static Tensor |
torch.rand(long[] size,
GeneratorOptional generator,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.rand(long[] size,
GeneratorOptional generator,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.rand(long[] size,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.rand(long[] size,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.rand(long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.rand(long[] size,
TensorOptions options) |
static Tensor |
torch.rand(LongArrayRef size) |
static Tensor |
torch.rand(LongArrayRef size,
DimnameListOptional names) |
static Tensor |
torch.rand(LongArrayRef size,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.rand(LongArrayRef size,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.rand(LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.rand(LongArrayRef size,
GeneratorOptional generator,
DimnameListOptional names) |
static Tensor |
torch.rand(LongArrayRef size,
GeneratorOptional generator,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.rand(LongArrayRef size,
GeneratorOptional generator,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.rand(LongArrayRef size,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.rand(LongArrayRef size,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.rand(LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.rand(LongArrayRef size,
TensorOptions options) |
static Tensor |
torch.randint_like(Tensor self,
long high) |
static Tensor |
torch.randint_like(Tensor self,
long low,
long high) |
static Tensor |
torch.randint_like(Tensor self,
long low,
long high,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randint_like(Tensor self,
long low,
long high,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randint_like(Tensor self,
long high,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randint_like(Tensor self,
long high,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randint_out(Tensor out,
long high,
long... size) |
static Tensor |
torch.randint_out(Tensor out,
long high,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.randint_out(Tensor out,
long high,
LongArrayRef size) |
static Tensor |
torch.randint_out(Tensor out,
long high,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.randint_out(Tensor out,
long low,
long high,
long... size) |
static Tensor |
torch.randint_out(Tensor out,
long low,
long high,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.randint_out(Tensor out,
long low,
long high,
LongArrayRef size) |
static Tensor |
torch.randint_out(Tensor out,
long low,
long high,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.randint_outf(long high,
long[] size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randint_outf(long high,
long[] size,
Tensor out) |
static Tensor |
torch.randint_outf(long high,
LongArrayRef size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randint_outf(long high,
LongArrayRef size,
Tensor out) |
static Tensor |
torch.randint_outf(long low,
long high,
long[] size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randint_outf(long low,
long high,
long[] size,
Tensor out) |
static Tensor |
torch.randint_outf(long low,
long high,
LongArrayRef size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randint_outf(long low,
long high,
LongArrayRef size,
Tensor out) |
static Tensor |
torch.randint(long high,
long... size) |
static Tensor |
torch.randint(long high,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.randint(long high,
long[] size,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randint(long high,
long[] size,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.randint(long high,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randint(long high,
long[] size,
TensorOptions options) |
static Tensor |
torch.randint(long high,
LongArrayRef size) |
static Tensor |
torch.randint(long high,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.randint(long high,
LongArrayRef size,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randint(long high,
LongArrayRef size,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.randint(long high,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randint(long high,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch.randint(long low,
long high,
long... size) |
static Tensor |
torch.randint(long low,
long high,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.randint(long low,
long high,
long[] size,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randint(long low,
long high,
long[] size,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.randint(long low,
long high,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randint(long low,
long high,
long[] size,
TensorOptions options) |
static Tensor |
torch.randint(long low,
long high,
LongArrayRef size) |
static Tensor |
torch.randint(long low,
long high,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.randint(long low,
long high,
LongArrayRef size,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randint(long low,
long high,
LongArrayRef size,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.randint(long low,
long high,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randint(long low,
long high,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch.randn_like(Tensor self) |
static Tensor |
torch.randn_like(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randn_like(Tensor self,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randn_out(Tensor out,
long... size) |
static Tensor |
torch.randn_out(Tensor out,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.randn_out(Tensor out,
LongArrayRef size) |
static Tensor |
torch.randn_out(Tensor out,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.randn_outf(long[] size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randn_outf(long[] size,
Tensor out) |
static Tensor |
torch.randn_outf(LongArrayRef size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randn_outf(LongArrayRef size,
Tensor out) |
static Tensor |
torch.randn(long... size) |
static Tensor |
torch.randn(long[] size,
DimnameListOptional names) |
static Tensor |
torch.randn(long[] size,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randn(long[] size,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.randn(long[] size,
GeneratorOptional generator) |
static Tensor |
torch.randn(long[] size,
GeneratorOptional generator,
DimnameListOptional names) |
static Tensor |
torch.randn(long[] size,
GeneratorOptional generator,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randn(long[] size,
GeneratorOptional generator,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.randn(long[] size,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randn(long[] size,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.randn(long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randn(long[] size,
TensorOptions options) |
static Tensor |
torch.randn(LongArrayRef size) |
static Tensor |
torch.randn(LongArrayRef size,
DimnameListOptional names) |
static Tensor |
torch.randn(LongArrayRef size,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randn(LongArrayRef size,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.randn(LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.randn(LongArrayRef size,
GeneratorOptional generator,
DimnameListOptional names) |
static Tensor |
torch.randn(LongArrayRef size,
GeneratorOptional generator,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randn(LongArrayRef size,
GeneratorOptional generator,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.randn(LongArrayRef size,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randn(LongArrayRef size,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.randn(LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randn(LongArrayRef size,
TensorOptions options) |
static Tensor |
torch.randperm_out(Tensor out,
long n) |
static Tensor |
torch.randperm_out(Tensor out,
long n,
GeneratorOptional generator) |
static Tensor |
torch.randperm_outf(long n,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randperm_outf(long n,
Tensor out) |
static Tensor |
torch.randperm(long n) |
static Tensor |
torch.randperm(long n,
GeneratorOptional generator) |
static Tensor |
torch.randperm(long n,
GeneratorOptional generator,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randperm(long n,
GeneratorOptional generator,
TensorOptions options) |
static Tensor |
torch.randperm(long n,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.randperm(long n,
TensorOptions options) |
static Tensor |
torch.range_out(Tensor out,
Scalar start,
Scalar end) |
static Tensor |
torch.range_out(Tensor out,
Scalar start,
Scalar end,
Scalar step) |
static Tensor |
torch.range_outf(Scalar start,
Scalar end,
Scalar step,
Tensor out) |
static Tensor |
torch.range(Scalar start,
Scalar end,
Scalar step,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.range(Scalar start,
Scalar end,
Scalar step,
TensorOptions options) |
static Tensor |
torch.range(Scalar start,
Scalar end,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.range(Scalar start,
Scalar end,
TensorOptions options) |
static Tensor |
torch.ravel(Tensor self) |
static Tensor |
torch.real(Tensor self) |
static Tensor |
torch.reciprocal_(Tensor self) |
static Tensor |
torch.reciprocal_out(Tensor out,
Tensor self) |
static Tensor |
torch.reciprocal_outf(Tensor self,
Tensor out) |
static Tensor |
torch.reciprocal(Tensor self) |
static Tensor |
torch.reflection_pad1d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad1d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad1d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad1d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad1d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad1d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad1d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad1d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad1d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.reflection_pad1d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.reflection_pad1d(Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad1d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad2d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad2d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad2d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad2d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad2d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad2d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad2d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.reflection_pad2d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.reflection_pad2d(Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad2d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad3d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad3d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad3d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad3d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad3d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad3d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad3d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.reflection_pad3d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.reflection_pad3d(Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad3d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.relu_(Tensor self) |
static Tensor |
torch.relu(Tensor self) |
static Tensor |
torch.relu(Tensor input,
boolean inplace) |
static Tensor |
torch.relu(Tensor input,
ReLUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.relu
/** about the exact behavior of this functional.
|
static Tensor |
torch.relu6_(Tensor self) |
static Tensor |
torch.relu6(Tensor self) |
static Tensor |
torch.relu6(Tensor input,
boolean inplace) |
static Tensor |
torch.relu6(Tensor input,
ReLU6Options options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.relu6
/** about the exact behavior of this functional.
|
static Tensor |
torch.remainder_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.remainder_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.remainder_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.remainder_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.remainder(Scalar self,
Tensor other) |
static Tensor |
torch.remainder(Tensor self,
Scalar other) |
static Tensor |
torch.remainder(Tensor self,
Tensor other) |
static Tensor |
torch.renorm_out(Tensor out,
Tensor self,
Scalar p,
long dim,
Scalar maxnorm) |
static Tensor |
torch.renorm_outf(Tensor self,
Scalar p,
long dim,
Scalar maxnorm,
Tensor out) |
static Tensor |
torch.renorm(Tensor self,
Scalar p,
long dim,
Scalar maxnorm) |
static Tensor |
torch.repeat_interleave(Tensor repeats) |
static Tensor |
torch.repeat_interleave(Tensor self,
long repeats) |
static Tensor |
torch.repeat_interleave(Tensor self,
long repeats,
LongOptional dim,
LongOptional output_size) |
static Tensor |
torch.repeat_interleave(Tensor repeats,
LongOptional output_size) |
static Tensor |
torch.repeat_interleave(Tensor self,
Tensor repeats) |
static Tensor |
torch.repeat_interleave(Tensor self,
Tensor repeats,
LongOptional dim,
LongOptional output_size) |
static Tensor |
torch.replication_pad1d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad1d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad1d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad1d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad1d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad1d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad1d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad1d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad1d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.replication_pad1d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.replication_pad1d(Tensor self,
long... padding) |
static Tensor |
torch.replication_pad1d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad2d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad2d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad2d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad2d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad2d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad2d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad2d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.replication_pad2d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.replication_pad2d(Tensor self,
long... padding) |
static Tensor |
torch.replication_pad2d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad3d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad3d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad3d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad3d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad3d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad3d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad3d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.replication_pad3d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.replication_pad3d(Tensor self,
long... padding) |
static Tensor |
torch.replication_pad3d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reshape(Tensor self,
long... shape) |
static Tensor |
torch.reshape(Tensor self,
LongArrayRef shape) |
static Tensor |
torch.resize_as_(Tensor self,
Tensor the_template) |
static Tensor |
torch.resize_as_(Tensor self,
Tensor the_template,
MemoryFormatOptional memory_format) |
static Tensor |
torch.resize_as_sparse_(Tensor self,
Tensor the_template) |
static Tensor |
torch.resolve_conj(Tensor self) |
static Tensor |
torch.resolve_neg(Tensor self) |
static Tensor |
torch.rnn_relu_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh) |
static Tensor |
torch.rnn_relu_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static Tensor |
torch.rnn_tanh_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh) |
static Tensor |
torch.rnn_tanh_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static Tensor |
torch.roll(Tensor self,
long... shifts) |
static Tensor |
torch.roll(Tensor self,
long[] shifts,
long... dims) |
static Tensor |
torch.roll(Tensor self,
LongArrayRef shifts) |
static Tensor |
torch.roll(Tensor self,
LongArrayRef shifts,
LongArrayRef dims) |
static Tensor |
torch.rot90(Tensor self) |
static Tensor |
torch.rot90(Tensor self,
long k,
long... dims) |
static Tensor |
torch.rot90(Tensor self,
long k,
LongArrayRef dims) |
static Tensor |
torch.round_(Tensor self) |
static Tensor |
torch.round_out(Tensor out,
Tensor self) |
static Tensor |
torch.round_outf(Tensor self,
Tensor out) |
static Tensor |
torch.round(Tensor self) |
static Tensor |
torch.row_stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.row_stack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.row_stack(TensorArrayRef tensors) |
static Tensor |
torch.rrelu_(Tensor self) |
static Tensor |
torch.rrelu_(Tensor self,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator) |
static Tensor |
torch.rrelu_with_noise_(Tensor self,
Tensor noise) |
static Tensor |
torch.rrelu_with_noise_(Tensor self,
Tensor noise,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator) |
static Tensor |
torch.rrelu_with_noise_backward(Tensor grad_output,
Tensor self,
Tensor noise,
Scalar lower,
Scalar upper,
boolean training,
boolean self_is_result) |
static Tensor |
torch.rrelu_with_noise_out(Tensor out,
Tensor self,
Tensor noise) |
static Tensor |
torch.rrelu_with_noise_out(Tensor out,
Tensor self,
Tensor noise,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator) |
static Tensor |
torch.rrelu_with_noise_outf(Tensor self,
Tensor noise,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.rrelu_with_noise(Tensor self,
Tensor noise) |
static Tensor |
torch.rrelu_with_noise(Tensor self,
Tensor noise,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator) |
static Tensor |
torch.rrelu(Tensor self) |
static Tensor |
torch.rrelu(Tensor input,
double lower,
double upper,
boolean training,
boolean inplace) |
static Tensor |
torch.rrelu(Tensor input,
RReLUFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.rrelu
/** about the exact behavior of this functional.
|
static Tensor |
torch.rrelu(Tensor self,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator) |
static Tensor |
torch.rsqrt_(Tensor self) |
static Tensor |
torch.rsqrt_out(Tensor out,
Tensor self) |
static Tensor |
torch.rsqrt_outf(Tensor self,
Tensor out) |
static Tensor |
torch.rsqrt(Tensor self) |
static Tensor |
torch.rsub(Tensor self,
Scalar other) |
static Tensor |
torch.rsub(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch.rsub(Tensor self,
Tensor other) |
static Tensor |
torch.rsub(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.scalar_tensor_static(Scalar s,
ScalarTypeOptional dtype_opt,
DeviceOptional device_opt) |
static Tensor |
torch.scalar_tensor(Scalar s) |
static Tensor |
torch.scalar_tensor(Scalar s,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.scalar_tensor(Scalar s,
TensorOptions options) |
static Tensor |
torch.scalar_to_tensor(Scalar s) |
static Tensor |
torch.scalar_to_tensor(Scalar s,
Device device) |
static Tensor |
torch.scalarToTensor(Scalar v,
TensorOptions options,
Device self_device) |
static Tensor |
torch.scalarToTensorNonNativeDeviceType(Scalar v,
TensorOptions options) |
static Tensor |
torch.scatter_add_out(Tensor out,
Tensor self,
long dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter_add_outf(Tensor self,
long dim,
Tensor index,
Tensor src,
Tensor out) |
static Tensor |
torch.scatter_add(Tensor self,
Dimname dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter_add(Tensor self,
long dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter_out(Tensor out,
Tensor self,
long dim,
Tensor index,
Scalar value) |
static Tensor |
torch.scatter_out(Tensor out,
Tensor self,
long dim,
Tensor index,
Scalar value,
Pointer reduce) |
static Tensor |
torch.scatter_out(Tensor out,
Tensor self,
long dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter_out(Tensor out,
Tensor self,
long dim,
Tensor index,
Tensor src,
Pointer reduce) |
static Tensor |
torch.scatter_outf(Tensor self,
long dim,
Tensor index,
Scalar value,
Pointer reduce,
Tensor out) |
static Tensor |
torch.scatter_outf(Tensor self,
long dim,
Tensor index,
Scalar value,
Tensor out) |
static Tensor |
torch.scatter_outf(Tensor self,
long dim,
Tensor index,
Tensor src,
Pointer reduce,
Tensor out) |
static Tensor |
torch.scatter_outf(Tensor self,
long dim,
Tensor index,
Tensor src,
Tensor out) |
static Tensor |
torch.scatter(Tensor self,
Dimname dim,
Tensor index,
Scalar value) |
static Tensor |
torch.scatter(Tensor self,
Dimname dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter(Tensor self,
long dim,
Tensor index,
Scalar value) |
static Tensor |
torch.scatter(Tensor self,
long dim,
Tensor index,
Scalar value,
Pointer reduce) |
static Tensor |
torch.scatter(Tensor self,
long dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter(Tensor self,
long dim,
Tensor index,
Tensor src,
Pointer reduce) |
static Tensor |
torch.searchsorted_out(Tensor out,
Tensor sorted_sequence,
Tensor self) |
static Tensor |
torch.searchsorted_out(Tensor out,
Tensor sorted_sequence,
Tensor self,
boolean out_int32,
boolean right) |
static Tensor |
torch.searchsorted_outf(Tensor sorted_sequence,
Tensor self,
boolean out_int32,
boolean right,
Tensor out) |
static Tensor |
torch.searchsorted(Tensor sorted_sequence,
Scalar self) |
static Tensor |
torch.searchsorted(Tensor sorted_sequence,
Scalar self,
boolean out_int32,
boolean right) |
static Tensor |
torch.searchsorted(Tensor sorted_sequence,
Tensor self) |
static Tensor |
torch.searchsorted(Tensor sorted_sequence,
Tensor self,
boolean out_int32,
boolean right) |
static Tensor |
torch.segment_reduce(Tensor data,
Pointer reduce) |
static Tensor |
torch.segment_reduce(Tensor data,
Pointer reduce,
TensorOptional lengths,
TensorOptional indices,
long axis,
boolean unsafe,
ScalarOptional initial) |
static Tensor |
torch.select_backward(Tensor grad_output,
long[] input_sizes,
long dim,
long index) |
static Tensor |
torch.select_backward(Tensor grad_output,
LongArrayRef input_sizes,
long dim,
long index) |
static Tensor |
torch.select(Tensor self,
Dimname dim,
long index) |
static Tensor |
torch.select(Tensor self,
long dim,
long index) |
static Tensor |
torch.selu_(Tensor self) |
static Tensor |
torch.selu(Tensor self) |
static Tensor |
torch.selu(Tensor input,
boolean inplace) |
static Tensor |
torch.selu(Tensor input,
SELUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.selu
/** about the exact behavior of this functional.
|
static Tensor |
torch.sgn_out(Tensor out,
Tensor self) |
static Tensor |
torch.sgn_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sgn(Tensor self) |
static Tensor |
torch.sigmoid_(Tensor self) |
static Tensor |
torch.sigmoid_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor output) |
static Tensor |
torch.sigmoid_backward_outf(Tensor grad_output,
Tensor output,
Tensor grad_input) |
static Tensor |
torch.sigmoid_backward(Tensor grad_output,
Tensor output) |
static Tensor |
torch.sigmoid_out(Tensor out,
Tensor self) |
static Tensor |
torch.sigmoid_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sigmoid(Tensor self) |
static Tensor |
torch.sign_out(Tensor out,
Tensor self) |
static Tensor |
torch.sign_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sign(Tensor self) |
static Tensor |
torch.signbit_out(Tensor out,
Tensor self) |
static Tensor |
torch.signbit_outf(Tensor self,
Tensor out) |
static Tensor |
torch.signbit(Tensor self) |
static Tensor |
torch.silu_(Tensor self) |
static Tensor |
torch.silu_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self) |
static Tensor |
torch.silu_backward_outf(Tensor grad_output,
Tensor self,
Tensor grad_input) |
static Tensor |
torch.silu_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.silu_out(Tensor out,
Tensor self) |
static Tensor |
torch.silu_outf(Tensor self,
Tensor out) |
static Tensor |
torch.silu(Tensor self) |
static Tensor |
torch.sin_(Tensor self) |
static Tensor |
torch.sin_out(Tensor out,
Tensor self) |
static Tensor |
torch.sin_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sin(Tensor self) |
static Tensor |
torch.sinc_(Tensor self) |
static Tensor |
torch.sinc_out(Tensor out,
Tensor self) |
static Tensor |
torch.sinc_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sinc(Tensor self) |
static Tensor |
torch.sinh_(Tensor self) |
static Tensor |
torch.sinh_out(Tensor out,
Tensor self) |
static Tensor |
torch.sinh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sinh(Tensor self) |
static Tensor |
torch.slice_backward(Tensor grad_output,
long[] input_sizes,
long dim,
long start,
long end,
long step) |
static Tensor |
torch.slice_backward(Tensor grad_output,
LongArrayRef input_sizes,
long dim,
long start,
long end,
long step) |
static Tensor |
torch.slice(Tensor self) |
static Tensor |
torch.slice(Tensor self,
long dim,
LongOptional start,
LongOptional end,
long step) |
static Tensor |
torch.slow_conv_dilated2d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_dilated2d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long... dilation) |
static Tensor |
torch.slow_conv_dilated2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_dilated2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static Tensor |
torch.slow_conv_dilated3d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_dilated3d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long... dilation) |
static Tensor |
torch.slow_conv_dilated3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_dilated3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static Tensor |
torch.slow_conv_transpose2d_out(Tensor out,
Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_transpose2d_out(Tensor out,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long... dilation) |
static Tensor |
torch.slow_conv_transpose2d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_transpose2d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation) |
static Tensor |
torch.slow_conv_transpose2d_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor out) |
static Tensor |
torch.slow_conv_transpose2d_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor out) |
static Tensor |
torch.slow_conv_transpose2d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_transpose2d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long... dilation) |
static Tensor |
torch.slow_conv_transpose2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_transpose2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation) |
static Tensor |
torch.slow_conv_transpose3d_out(Tensor out,
Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_transpose3d_out(Tensor out,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long... dilation) |
static Tensor |
torch.slow_conv_transpose3d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_transpose3d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation) |
static Tensor |
torch.slow_conv_transpose3d_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor out) |
static Tensor |
torch.slow_conv_transpose3d_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor out) |
static Tensor |
torch.slow_conv_transpose3d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_transpose3d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long... dilation) |
static Tensor |
torch.slow_conv_transpose3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_transpose3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation) |
static Tensor |
torch.slow_conv3d_out(Tensor out,
Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv3d_out(Tensor out,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static Tensor |
torch.slow_conv3d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv3d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.slow_conv3d_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
Tensor out) |
static Tensor |
torch.slow_conv3d_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.slow_conv3d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv3d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static Tensor |
torch.slow_conv3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.smm(Tensor self,
Tensor mat2) |
static Tensor |
torch.smooth_l1_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double beta) |
static Tensor |
torch.smooth_l1_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double beta,
Tensor grad_input) |
static Tensor |
torch.smooth_l1_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double beta) |
static Tensor |
torch.smooth_l1_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.smooth_l1_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction,
double beta) |
static Tensor |
torch.smooth_l1_loss_outf(Tensor self,
Tensor target,
long reduction,
double beta,
Tensor out) |
static Tensor |
torch.smooth_l1_loss(Tensor self,
Tensor target) |
static Tensor |
torch.smooth_l1_loss(Tensor self,
Tensor target,
long reduction,
double beta) |
static Tensor |
torch.smooth_l1_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.smooth_l1_loss(Tensor input,
Tensor target,
loss_reduction_t reduction,
double beta) |
static Tensor |
torch.smooth_l1_loss(Tensor input,
Tensor target,
SmoothL1LossOptions options,
double beta)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.smooth_l1_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.soft_margin_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.soft_margin_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor grad_input) |
static Tensor |
torch.soft_margin_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.soft_margin_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.soft_margin_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.soft_margin_loss_outf(Tensor self,
Tensor target,
long reduction,
Tensor out) |
static Tensor |
torch.soft_margin_loss(Tensor self,
Tensor target) |
static Tensor |
torch.soft_margin_loss(Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.soft_margin_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.soft_margin_loss(Tensor input,
Tensor target,
SoftMarginLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.soft_margin_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.softmax(Tensor self,
Dimname dim) |
static Tensor |
torch.softmax(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.softmax(Tensor self,
long dim) |
static Tensor |
torch.softmax(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.softmax(Tensor input,
SoftmaxFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softmax
/** about the exact behavior of this functional.
|
static Tensor |
torch.softmin(Tensor input,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.softmin(Tensor input,
SoftminFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softmin
/** about the exact behavior of this functional.
|
static Tensor |
torch.softplus_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Scalar beta,
Scalar threshold,
Tensor output) |
static Tensor |
torch.softplus_backward_outf(Tensor grad_output,
Tensor self,
Scalar beta,
Scalar threshold,
Tensor output,
Tensor grad_input) |
static Tensor |
torch.softplus_backward(Tensor grad_output,
Tensor self,
Scalar beta,
Scalar threshold,
Tensor output) |
static Tensor |
torch.softplus_out(Tensor out,
Tensor self) |
static Tensor |
torch.softplus_out(Tensor out,
Tensor self,
Scalar beta,
Scalar threshold) |
static Tensor |
torch.softplus_outf(Tensor self,
Scalar beta,
Scalar threshold,
Tensor out) |
static Tensor |
torch.softplus(Tensor self) |
static Tensor |
torch.softplus(Tensor input,
double beta,
double threshold) |
static Tensor |
torch.softplus(Tensor self,
Scalar beta,
Scalar threshold) |
static Tensor |
torch.softplus(Tensor input,
SoftplusOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softplus
/** about the exact behavior of this functional.
|
static Tensor |
torch.softshrink_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Scalar lambd) |
static Tensor |
torch.softshrink_backward_outf(Tensor grad_output,
Tensor self,
Scalar lambd,
Tensor grad_input) |
static Tensor |
torch.softshrink_backward(Tensor grad_output,
Tensor self,
Scalar lambd) |
static Tensor |
torch.softshrink_out(Tensor out,
Tensor self) |
static Tensor |
torch.softshrink_out(Tensor out,
Tensor self,
Scalar lambd) |
static Tensor |
torch.softshrink_outf(Tensor self,
Scalar lambd,
Tensor out) |
static Tensor |
torch.softshrink(Tensor self) |
static Tensor |
torch.softshrink(Tensor input,
double lambda) |
static Tensor |
torch.softshrink(Tensor self,
Scalar lambd) |
static Tensor |
torch.softshrink(Tensor input,
SoftshrinkOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softshrink
/** about the exact behavior of this functional.
|
static Tensor |
torch.softsign(Tensor input) |
static Tensor |
torch.sparse_(Tensor tensor,
double sparsity) |
static Tensor |
torch.sparse_(Tensor tensor,
double sparsity,
double std)
Fills the 2D input
Tensor as a sparse matrix, where the
non-zero elements will be drawn from a centered normal distribution
with the given standard deviation std, as described in "Deep learning via
Hessian-free optimization" - Martens, J. |
static Tensor |
torch.sparse_coo_tensor(long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_coo_tensor(long[] size,
TensorOptions options) |
static Tensor |
torch.sparse_coo_tensor(LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_coo_tensor(LongArrayRef size,
TensorOptions options) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
long... size) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
long[] size,
TensorOptions options) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
LongArrayRef size) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
TensorOptions options) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long[] size,
TensorOptions options) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
TensorOptions options) |
static Tensor |
torch.special_digamma_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_digamma_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_digamma(Tensor self) |
static Tensor |
torch.special_entr_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_entr_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_entr(Tensor self) |
static Tensor |
torch.special_erf_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_erf_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_erf(Tensor self) |
static Tensor |
torch.special_erfc_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_erfc_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_erfc(Tensor self) |
static Tensor |
torch.special_erfcx_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_erfcx_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_erfcx(Tensor self) |
static Tensor |
torch.special_erfinv_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_erfinv_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_erfinv(Tensor self) |
static Tensor |
torch.special_exp2_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_exp2_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_exp2(Tensor self) |
static Tensor |
torch.special_expit_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_expit_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_expit(Tensor self) |
static Tensor |
torch.special_expm1_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_expm1_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_expm1(Tensor self) |
static Tensor |
torch.special_gammainc_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.special_gammainc_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_gammainc(Tensor self,
Tensor other) |
static Tensor |
torch.special_gammaincc_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.special_gammaincc_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_gammaincc(Tensor self,
Tensor other) |
static Tensor |
torch.special_gammaln_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_gammaln_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_gammaln(Tensor self) |
static Tensor |
torch.special_i0_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_i0_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_i0(Tensor self) |
static Tensor |
torch.special_i0e_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_i0e_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_i0e(Tensor self) |
static Tensor |
torch.special_i1_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_i1_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_i1(Tensor self) |
static Tensor |
torch.special_i1e_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_i1e_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_i1e(Tensor self) |
static Tensor |
torch.special_log_softmax(Tensor self,
long dim) |
static Tensor |
torch.special_log_softmax(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.special_log1p_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_log1p_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_log1p(Tensor self) |
static Tensor |
torch.special_logit_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_logit_out(Tensor out,
Tensor self,
DoubleOptional eps) |
static Tensor |
torch.special_logit_outf(Tensor self,
DoubleOptional eps,
Tensor out) |
static Tensor |
torch.special_logit(Tensor self) |
static Tensor |
torch.special_logit(Tensor self,
DoubleOptional eps) |
static Tensor |
torch.special_logsumexp_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.special_logsumexp_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.special_logsumexp_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.special_logsumexp_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.special_logsumexp_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.special_logsumexp_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.special_logsumexp(Tensor self,
long... dim) |
static Tensor |
torch.special_logsumexp(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.special_logsumexp(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.special_logsumexp(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.special_multigammaln_out(Tensor out,
Tensor self,
long p) |
static Tensor |
torch.special_multigammaln_outf(Tensor self,
long p,
Tensor out) |
static Tensor |
torch.special_multigammaln(Tensor self,
long p) |
static Tensor |
torch.special_ndtr_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_ndtr_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_ndtr(Tensor self) |
static Tensor |
torch.special_ndtri_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_ndtri_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_ndtri(Tensor self) |
static Tensor |
torch.special_polygamma_out(Tensor out,
long n,
Tensor self) |
static Tensor |
torch.special_polygamma_outf(long n,
Tensor self,
Tensor out) |
static Tensor |
torch.special_polygamma(long n,
Tensor self) |
static Tensor |
torch.special_psi_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_psi_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_psi(Tensor self) |
static Tensor |
torch.special_round_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_round_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_round(Tensor self) |
static Tensor |
torch.special_sinc_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_sinc_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_sinc(Tensor self) |
static Tensor |
torch.special_xlog1py_out(Tensor out,
Scalar self,
Tensor other) |
static Tensor |
torch.special_xlog1py_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.special_xlog1py_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.special_xlog1py_outf(Scalar self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_xlog1py_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.special_xlog1py_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_xlog1py(Scalar self,
Tensor other) |
static Tensor |
torch.special_xlog1py(Tensor self,
Scalar other) |
static Tensor |
torch.special_xlog1py(Tensor self,
Tensor other) |
static Tensor |
torch.special_xlogy_out(Tensor out,
Scalar self,
Tensor other) |
static Tensor |
torch.special_xlogy_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.special_xlogy_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.special_xlogy_outf(Scalar self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_xlogy_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.special_xlogy_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_xlogy(Scalar self,
Tensor other) |
static Tensor |
torch.special_xlogy(Tensor self,
Scalar other) |
static Tensor |
torch.special_xlogy(Tensor self,
Tensor other) |
static Tensor |
torch.special_zeta_out(Tensor out,
Scalar self,
Tensor other) |
static Tensor |
torch.special_zeta_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.special_zeta_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.special_zeta_outf(Scalar self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_zeta_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.special_zeta_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_zeta(Scalar self,
Tensor other) |
static Tensor |
torch.special_zeta(Tensor self,
Scalar other) |
static Tensor |
torch.special_zeta(Tensor self,
Tensor other) |
static Tensor |
torch.sqrt_(Tensor self) |
static Tensor |
torch.sqrt_out(Tensor out,
Tensor self) |
static Tensor |
torch.sqrt_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sqrt(Tensor self) |
static Tensor |
torch.square_(Tensor self) |
static Tensor |
torch.square_out(Tensor out,
Tensor self) |
static Tensor |
torch.square_outf(Tensor self,
Tensor out) |
static Tensor |
torch.square(Tensor self) |
static Tensor |
torch.squeeze(Tensor self) |
static Tensor |
torch.squeeze(Tensor self,
Dimname dim) |
static Tensor |
torch.squeeze(Tensor self,
long dim) |
static Tensor |
torch.sspaddmm_out(Tensor out,
Tensor self,
Tensor mat1,
Tensor mat2) |
static Tensor |
torch.sspaddmm_out(Tensor out,
Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.sspaddmm_outf(Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.sspaddmm(Tensor self,
Tensor mat1,
Tensor mat2) |
static Tensor |
torch.sspaddmm(Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.stack_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch.stack_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch.stack(TensorArrayRef tensors) |
static Tensor |
torch.stack(TensorArrayRef tensors,
long dim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
LongOptional correction) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
LongArrayRefOptional dim,
LongOptional correction) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.std_outf(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.std_outf(Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim,
Tensor out) |
static Tensor |
torch.std_outf(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.std_outf(Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.std_outf(Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim,
Tensor out) |
static Tensor |
torch.std(Tensor self) |
static Tensor |
torch.std(Tensor self,
boolean unbiased) |
static Tensor |
torch.std(Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.std(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std(Tensor self,
DimnameArrayRef dim,
LongOptional correction) |
static Tensor |
torch.std(Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.std(Tensor self,
int dim) |
static Tensor |
torch.std(Tensor self,
long... dim) |
static Tensor |
torch.std(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.std(Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std(Tensor self,
LongArrayRefOptional dim,
LongOptional correction) |
static Tensor |
torch.std(Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.stft(Tensor self,
long n_fft) |
static Tensor |
torch.stft(Tensor self,
long n_fft,
LongOptional hop_length,
LongOptional win_length,
TensorOptional window,
boolean normalized,
BoolOptional onesided,
BoolOptional return_complex) |
static Tensor |
torch.sub_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.sub_out(Tensor out,
Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.sub_outf(Tensor self,
Tensor other,
Scalar alpha,
Tensor out) |
static Tensor |
torch.sub(Tensor self,
Scalar other) |
static Tensor |
torch.sub(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch.sub(Tensor self,
Tensor other) |
static Tensor |
torch.sub(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.subtract_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.subtract_out(Tensor out,
Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.subtract_outf(Tensor self,
Tensor other,
Scalar alpha,
Tensor out) |
static Tensor |
torch.subtract(Scalar x,
Tensor y) |
static Tensor |
torch.subtract(Tensor self,
Scalar other) |
static Tensor |
torch.subtract(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch.subtract(Tensor self,
Tensor other) |
static Tensor |
torch.subtract(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum_outf(Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.sum_outf(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.sum_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.sum_to(Tensor tensor,
long... shape) |
static Tensor |
torch.sum_to(Tensor tensor,
LongArrayRef shape) |
static Tensor |
torch.sum(Tensor self) |
static Tensor |
torch.sum(Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.sum(Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum(Tensor self,
long... dim) |
static Tensor |
torch.sum(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.sum(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum(Tensor self,
ScalarTypeOptional dtype) |
static Tensor |
torch.swapaxes(Tensor self,
long axis0,
long axis1) |
static Tensor |
torch.swapdims(Tensor self,
long dim0,
long dim1) |
static Tensor |
torch.t(Tensor self) |
static Tensor |
torch.take_along_dim_out(Tensor out,
Tensor self,
Tensor indices) |
static Tensor |
torch.take_along_dim_out(Tensor out,
Tensor self,
Tensor indices,
LongOptional dim) |
static Tensor |
torch.take_along_dim_outf(Tensor self,
Tensor indices,
LongOptional dim,
Tensor out) |
static Tensor |
torch.take_along_dim(Tensor self,
Tensor indices) |
static Tensor |
torch.take_along_dim(Tensor self,
Tensor indices,
LongOptional dim) |
static Tensor |
torch.take_out(Tensor out,
Tensor self,
Tensor index) |
static Tensor |
torch.take_outf(Tensor self,
Tensor index,
Tensor out) |
static Tensor |
torch.take(Tensor self,
Tensor index) |
static Tensor |
torch.tan_(Tensor self) |
static Tensor |
torch.tan_out(Tensor out,
Tensor self) |
static Tensor |
torch.tan_outf(Tensor self,
Tensor out) |
static Tensor |
torch.tan(Tensor self) |
static Tensor |
torch.tanh_(Tensor self) |
static Tensor |
torch.tanh_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor output) |
static Tensor |
torch.tanh_backward_outf(Tensor grad_output,
Tensor output,
Tensor grad_input) |
static Tensor |
torch.tanh_backward(Tensor grad_output,
Tensor output) |
static Tensor |
torch.tanh_out(Tensor out,
Tensor self) |
static Tensor |
torch.tanh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.tanh(Tensor self) |
static Tensor |
torch.tanhshrink(Tensor input) |
static Tensor |
torch.tensor(BFloat16 value) |
static Tensor |
torch.tensor(BFloat16ArrayRef values) |
static Tensor |
torch.tensor(BFloat16ArrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(BFloat16 value,
TensorOptions options) |
static Tensor |
torch.tensor(BoolArrayRef values) |
static Tensor |
torch.tensor(BoolArrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(boolean value) |
static Tensor |
torch.tensor(boolean value,
TensorOptions options) |
static Tensor |
torch.tensor(byte value) |
static Tensor |
torch.tensor(ByteArrayRef values) |
static Tensor |
torch.tensor(ByteArrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(byte value,
TensorOptions options) |
static Tensor |
torch.tensor(double value) |
static Tensor |
torch.tensor(DoubleArrayRef values) |
static Tensor |
torch.tensor(DoubleArrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(DoubleComplexrrayRef values) |
static Tensor |
torch.tensor(DoubleComplexrrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(double value,
TensorOptions options) |
static Tensor |
torch.tensor(float value) |
static Tensor |
torch.tensor(FloatArrayRef values) |
static Tensor |
torch.tensor(FloatArrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(FloatComplexrrayRef values) |
static Tensor |
torch.tensor(FloatComplexrrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(float value,
TensorOptions options) |
static Tensor |
torch.tensor(Half value) |
static Tensor |
torch.tensor(HalfArrayRef values) |
static Tensor |
torch.tensor(HalfArrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(Half value,
TensorOptions options) |
static Tensor |
torch.tensor(int value) |
static Tensor |
torch.tensor(IntArrayRef values) |
static Tensor |
torch.tensor(IntArrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(int value,
TensorOptions options) |
static Tensor |
torch.tensor(long... values) |
static Tensor |
torch.tensor(long value) |
static Tensor |
torch.tensor(long[] values,
TensorOptions options) |
static Tensor |
torch.tensor(LongArrayRef values) |
static Tensor |
torch.tensor(LongArrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(long value,
TensorOptions options) |
static Tensor |
torch.tensor(Pointer tensor_data_container) |
static Tensor |
torch.tensor(Pointer tensor_data_container,
TensorOptions options)
NOTE: Currently
torch::tensor(...) doesn't support mixed data types
(i.e. |
static Tensor |
torch.tensor(short value) |
static Tensor |
torch.tensor(ShortArrayRef values) |
static Tensor |
torch.tensor(ShortArrayRef values,
TensorOptions options) |
static Tensor |
torch.tensor(short value,
TensorOptions options) |
static Tensor |
torch.tensordot_out(Tensor out,
Tensor self,
Tensor other,
long[] dims_self,
long... dims_other) |
static Tensor |
torch.tensordot_out(Tensor out,
Tensor self,
Tensor other,
LongArrayRef dims_self,
LongArrayRef dims_other) |
static Tensor |
torch.tensordot_outf(Tensor self,
Tensor other,
long[] dims_self,
long[] dims_other,
Tensor out) |
static Tensor |
torch.tensordot_outf(Tensor self,
Tensor other,
LongArrayRef dims_self,
LongArrayRef dims_other,
Tensor out) |
static Tensor |
torch.tensordot(Tensor self,
Tensor other,
long[] dims_self,
long... dims_other) |
static Tensor |
torch.tensordot(Tensor self,
Tensor other,
LongArrayRef dims_self,
LongArrayRef dims_other) |
static Tensor |
torch.thnn_conv2d_out(Tensor out,
Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.thnn_conv2d_out(Tensor out,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static Tensor |
torch.thnn_conv2d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.thnn_conv2d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.thnn_conv2d_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
Tensor out) |
static Tensor |
torch.thnn_conv2d_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.thnn_conv2d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.thnn_conv2d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static Tensor |
torch.thnn_conv2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.thnn_conv2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.threshold_(Tensor self,
Scalar threshold,
Scalar value) |
static Tensor |
torch.threshold_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Scalar threshold) |
static Tensor |
torch.threshold_backward_outf(Tensor grad_output,
Tensor self,
Scalar threshold,
Tensor grad_input) |
static Tensor |
torch.threshold_backward(Tensor grad_output,
Tensor self,
Scalar threshold) |
static Tensor |
torch.threshold_out(Tensor out,
Tensor self,
Scalar threshold,
Scalar value) |
static Tensor |
torch.threshold_outf(Tensor self,
Scalar threshold,
Scalar value,
Tensor out) |
static Tensor |
torch.threshold(Tensor input,
double threshold,
double value,
boolean inplace) |
static Tensor |
torch.threshold(Tensor self,
Scalar threshold,
Scalar value) |
static Tensor |
torch.threshold(Tensor input,
ThresholdOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.threshold
/** about the exact behavior of this functional.
|
static Tensor |
torch.tile(Tensor self,
long... dims) |
static Tensor |
torch.tile(Tensor self,
LongArrayRef dims) |
static Tensor |
torch.to_dense_backward(Tensor grad,
Tensor input) |
static Tensor |
torch.to_mkldnn_backward(Tensor grad,
Tensor input) |
static Tensor |
torch.trace_backward(Tensor grad,
long... sizes) |
static Tensor |
torch.trace_backward(Tensor grad,
LongArrayRef sizes) |
static Tensor |
torch.trace(Tensor self) |
static Tensor |
torch.transpose(Tensor self,
Dimname dim0,
Dimname dim1) |
static Tensor |
torch.transpose(Tensor self,
long dim0,
long dim1) |
static Tensor |
torch.trapezoid(Tensor y) |
static Tensor |
torch.trapezoid(Tensor y,
Scalar dx,
long dim) |
static Tensor |
torch.trapezoid(Tensor y,
Tensor x) |
static Tensor |
torch.trapezoid(Tensor y,
Tensor x,
long dim) |
static Tensor |
torch.trapz(Tensor y) |
static Tensor |
torch.trapz(Tensor y,
double dx,
long dim) |
static Tensor |
torch.trapz(Tensor y,
Tensor x) |
static Tensor |
torch.trapz(Tensor y,
Tensor x,
long dim) |
static Tensor |
torch.tril_indices(long row,
long col) |
static Tensor |
torch.tril_indices(long row,
long col,
long offset,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.tril_indices(long row,
long col,
long offset,
TensorOptions options) |
static Tensor |
torch.tril_out(Tensor out,
Tensor self) |
static Tensor |
torch.tril_out(Tensor out,
Tensor self,
long diagonal) |
static Tensor |
torch.tril_outf(Tensor self,
long diagonal,
Tensor out) |
static Tensor |
torch.tril(Tensor self) |
static Tensor |
torch.tril(Tensor self,
long diagonal) |
static Tensor |
torch.triplet_margin_loss(Tensor anchor,
Tensor positive,
Tensor negative) |
static Tensor |
torch.triplet_margin_loss(Tensor anchor,
Tensor positive,
Tensor negative,
double margin,
double p,
double eps,
boolean swap,
long reduction) |
static Tensor |
torch.triplet_margin_loss(Tensor anchor,
Tensor positive,
Tensor negative,
double margin,
double p,
double eps,
boolean swap,
loss_reduction_t reduction) |
static Tensor |
torch.triplet_margin_loss(Tensor anchor,
Tensor positive,
Tensor negative,
TripletMarginLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.triplet_margin_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.triplet_margin_with_distance_loss(Tensor anchor,
Tensor positive,
Tensor negative) |
static Tensor |
torch.triplet_margin_with_distance_loss(Tensor anchor,
Tensor positive,
Tensor negative,
Pointer distance_function,
double margin,
boolean swap,
loss_reduction_t reduction) |
static Tensor |
torch.triplet_margin_with_distance_loss(Tensor anchor,
Tensor positive,
Tensor negative,
TripletMarginWithDistanceLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.triplet_margin_with_distance_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.triu_indices(long row,
long col) |
static Tensor |
torch.triu_indices(long row,
long col,
long offset,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.triu_indices(long row,
long col,
long offset,
TensorOptions options) |
static Tensor |
torch.triu_out(Tensor out,
Tensor self) |
static Tensor |
torch.triu_out(Tensor out,
Tensor self,
long diagonal) |
static Tensor |
torch.triu_outf(Tensor self,
long diagonal,
Tensor out) |
static Tensor |
torch.triu(Tensor self) |
static Tensor |
torch.triu(Tensor self,
long diagonal) |
static Tensor |
torch.true_divide_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.true_divide_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.true_divide(Tensor self,
Scalar other) |
static Tensor |
torch.true_divide(Tensor self,
Tensor other) |
static Tensor |
torch.trunc_(Tensor self) |
static Tensor |
torch.trunc_out(Tensor out,
Tensor self) |
static Tensor |
torch.trunc_outf(Tensor self,
Tensor out) |
static Tensor |
torch.trunc(Tensor self) |
static Tensor |
torch.unfold_backward(Tensor grad_in,
long[] input_sizes,
long dim,
long size,
long step) |
static Tensor |
torch.unfold_backward(Tensor grad_in,
LongArrayRef input_sizes,
long dim,
long size,
long step) |
static Tensor |
torch.unfold(Tensor input,
LongPointer kernel_size,
LongPointer dilation,
LongPointer padding,
LongPointer stride) |
static Tensor |
torch.unfold(Tensor input,
UnfoldOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.unfold
/** about the exact behavior of this functional.
|
static Tensor |
torch.uniform_(Tensor tensor) |
static Tensor |
torch.uniform_(Tensor tensor,
double low,
double high)
Fills the given 2-dimensional
matrix with values drawn from a uniform
distribution parameterized by low and high. |
static Tensor |
torch.unsafeTensorFromTH(Pointer th_pointer,
boolean retain) |
static Tensor |
torch.unsqueeze(Tensor self,
long dim) |
static Tensor |
torch.upsample_bicubic2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_bicubic2d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_bicubic2d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_outf(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_bicubic2d_outf(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_bicubic2d(Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d(Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d(Tensor input,
LongArrayRefOptional output_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_bilinear2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_bilinear2d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_bilinear2d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_outf(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_bilinear2d_outf(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_bilinear2d(Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d(Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d(Tensor input,
LongArrayRefOptional output_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_linear1d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales,
Tensor grad_input) |
static Tensor |
torch.upsample_linear1d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales,
Tensor grad_input) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_linear1d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_outf(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales,
Tensor out) |
static Tensor |
torch.upsample_linear1d_outf(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales,
Tensor out) |
static Tensor |
torch.upsample_linear1d(Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d(Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d(Tensor input,
LongArrayRefOptional output_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest1d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest1d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest1d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest1d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest1d_out(Tensor out,
Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest1d_out(Tensor out,
Tensor self,
long[] output_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_out(Tensor out,
Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest1d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_outf(Tensor self,
long[] output_size,
DoubleOptional scales,
Tensor out) |
static Tensor |
torch.upsample_nearest1d_outf(Tensor self,
LongArrayRef output_size,
DoubleOptional scales,
Tensor out) |
static Tensor |
torch.upsample_nearest1d(Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest1d(Tensor self,
long[] output_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest1d(Tensor self,
LongArrayRef output_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d(Tensor input,
LongArrayRefOptional output_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest2d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest2d_out(Tensor out,
Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest2d_out(Tensor out,
Tensor self,
long[] output_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_out(Tensor out,
Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest2d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_outf(Tensor self,
long[] output_size,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_nearest2d_outf(Tensor self,
LongArrayRef output_size,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_nearest2d(Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest2d(Tensor self,
long[] output_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest2d(Tensor self,
LongArrayRef output_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d(Tensor input,
LongArrayRefOptional output_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest3d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest3d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest3d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest3d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest3d_out(Tensor out,
Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest3d_out(Tensor out,
Tensor self,
long[] output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_out(Tensor out,
Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest3d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_outf(Tensor self,
long[] output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_nearest3d_outf(Tensor self,
LongArrayRef output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_nearest3d(Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest3d(Tensor self,
long[] output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest3d(Tensor self,
LongArrayRef output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d(Tensor input,
LongArrayRefOptional output_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_trilinear3d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_trilinear3d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_trilinear3d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_outf(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_trilinear3d_outf(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_trilinear3d(Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d(Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d(Tensor input,
LongArrayRefOptional output_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.value_selecting_reduction_backward(Tensor grad,
long dim,
Tensor indices,
long[] sizes,
boolean keepdim) |
static Tensor |
torch.value_selecting_reduction_backward(Tensor grad,
long dim,
Tensor indices,
LongArrayRef sizes,
boolean keepdim) |
static Tensor |
torch.vander(Tensor x) |
static Tensor |
torch.vander(Tensor x,
LongOptional N,
boolean increasing) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
LongOptional correction) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
LongArrayRefOptional dim,
LongOptional correction) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.var_outf(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.var_outf(Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim,
Tensor out) |
static Tensor |
torch.var_outf(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.var_outf(Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.var_outf(Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim,
Tensor out) |
static Tensor |
torch.var(Tensor self) |
static Tensor |
torch.var(Tensor self,
boolean unbiased) |
static Tensor |
torch.var(Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.var(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var(Tensor self,
DimnameArrayRef dim,
LongOptional correction) |
static Tensor |
torch.var(Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.var(Tensor self,
int dim) |
static Tensor |
torch.var(Tensor self,
long... dim) |
static Tensor |
torch.var(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.var(Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var(Tensor self,
LongArrayRefOptional dim,
LongOptional correction) |
static Tensor |
torch.var(Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.vdot_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.vdot_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.vdot(Tensor self,
Tensor other) |
static Tensor |
torch.view_as_complex(Tensor self) |
static Tensor |
torch.view_as_real(Tensor self) |
static Tensor |
torch.vstack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.vstack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.vstack(TensorArrayRef tensors) |
static Tensor |
torch.where(Tensor condition,
Scalar self,
Scalar other) |
static Tensor |
torch.where(Tensor condition,
Scalar self,
Tensor other) |
static Tensor |
torch.where(Tensor condition,
Tensor self,
Scalar other) |
static Tensor |
torch.where(Tensor condition,
Tensor self,
Tensor other) |
static Tensor |
torch.wrapped_scalar_tensor(Scalar scalar) |
static Tensor |
torch.wrapped_scalar_tensor(Scalar scalar,
Device device) |
static Tensor |
torch.xavier_normal_(Tensor tensor) |
static Tensor |
torch.xavier_normal_(Tensor tensor,
double gain)
Fills the input
Tensor with values according to the method
described in "Understanding the difficulty of training deep feedforward
neural networks" - Glorot, X. |
static Tensor |
torch.xavier_uniform_(Tensor tensor) |
static Tensor |
torch.xavier_uniform_(Tensor tensor,
double gain)
Fills the input
Tensor with values according to the method
described in "Understanding the difficulty of training deep feedforward
neural networks" - Glorot, X. |
static Tensor |
torch.xlogy_(Tensor self,
Scalar other) |
static Tensor |
torch.xlogy_(Tensor self,
Tensor other) |
static Tensor |
torch.xlogy_out(Tensor out,
Scalar self,
Tensor other) |
static Tensor |
torch.xlogy_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.xlogy_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.xlogy_outf(Scalar self,
Tensor other,
Tensor out) |
static Tensor |
torch.xlogy_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.xlogy_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.xlogy(Scalar self,
Tensor other) |
static Tensor |
torch.xlogy(Tensor self,
Scalar other) |
static Tensor |
torch.xlogy(Tensor self,
Tensor other) |
static Tensor |
torch.xor(Scalar x,
Tensor y) |
static Tensor |
torch.xor(Tensor x,
Scalar y) |
static Tensor |
torch.xor(Tensor x,
Tensor y) |
static Tensor |
torch.zero_(Tensor self) |
static Tensor |
torch.zeros_(Tensor tensor)
Fills the given
tensor with zeros. |
static Tensor |
torch.zeros_like(Tensor self) |
static Tensor |
torch.zeros_like(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.zeros_like(Tensor self,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.zeros_out(Tensor out,
long... size) |
static Tensor |
torch.zeros_out(Tensor out,
LongArrayRef size) |
static Tensor |
torch.zeros_outf(long[] size,
Tensor out) |
static Tensor |
torch.zeros_outf(LongArrayRef size,
Tensor out) |
static Tensor |
torch.zeros(long... size) |
static Tensor |
torch.zeros(long[] size,
DimnameListOptional names) |
static Tensor |
torch.zeros(long[] size,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.zeros(long[] size,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.zeros(long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.zeros(long[] size,
TensorOptions options) |
static Tensor |
torch.zeros(LongArrayRef size) |
static Tensor |
torch.zeros(LongArrayRef size,
DimnameListOptional names) |
static Tensor |
torch.zeros(LongArrayRef size,
DimnameListOptional names,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.zeros(LongArrayRef size,
DimnameListOptional names,
TensorOptions options) |
static Tensor |
torch.zeros(LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.zeros(LongArrayRef size,
TensorOptions options) |
| Modifier and Type | Method and Description |
|---|---|
static PointerPointer<Tensor> |
torch._conv_depthwise2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long... dilation) |
static PointerPointer<Tensor> |
torch._conv_depthwise2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static PointerPointer<Tensor> |
torch._conv_depthwise2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
Tensor grad_input,
Tensor grad_weight) |
static PointerPointer<Tensor> |
torch._conv_depthwise2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
Tensor grad_input,
Tensor grad_weight) |
static PointerPointer<Tensor> |
torch._slow_conv2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
Tensor finput) |
static PointerPointer<Tensor> |
torch._slow_conv2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor finput) |
static PointerPointer<Tensor> |
torch._slow_conv2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
Tensor finput,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch._slow_conv2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor finput,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch._slow_conv2d_forward_out(Tensor output,
Tensor finput,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static PointerPointer<Tensor> |
torch._slow_conv2d_forward_out(Tensor output,
Tensor finput,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static PointerPointer<Tensor> |
torch._slow_conv2d_forward_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
Tensor output,
Tensor finput) |
static PointerPointer<Tensor> |
torch._slow_conv2d_forward_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
Tensor output,
Tensor finput) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool2d_out(Tensor out,
Tensor indices,
Tensor self,
long... output_size) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool2d_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef output_size) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool2d_outf(Tensor self,
long[] output_size,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool2d_outf(Tensor self,
LongArrayRef output_size,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool3d_out(Tensor out,
Tensor indices,
Tensor self,
long... output_size) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool3d_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef output_size) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool3d_outf(Tensor self,
long[] output_size,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool3d_outf(Tensor self,
LongArrayRef output_size,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.aminmax_out(Tensor min,
Tensor max,
Tensor self) |
static PointerPointer<Tensor> |
torch.aminmax_out(Tensor min,
Tensor max,
Tensor self,
LongOptional dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.aminmax_outf(Tensor self,
LongOptional dim,
boolean keepdim,
Tensor min,
Tensor max) |
static PointerPointer<Tensor> |
torch.conv_depthwise3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long... dilation) |
static PointerPointer<Tensor> |
torch.conv_depthwise3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static PointerPointer<Tensor> |
torch.conv_depthwise3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.conv_depthwise3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.cummax_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.cummax_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.cummax_outf(Tensor self,
Dimname dim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.cummax_outf(Tensor self,
long dim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.cummin_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.cummin_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.cummin_outf(Tensor self,
Dimname dim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.cummin_outf(Tensor self,
long dim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.eig_out(Tensor e,
Tensor v,
Tensor self) |
static PointerPointer<Tensor> |
torch.eig_out(Tensor e,
Tensor v,
Tensor self,
boolean eigenvectors) |
static PointerPointer<Tensor> |
torch.eig_outf(Tensor self,
boolean eigenvectors,
Tensor e,
Tensor v) |
static PointerPointer<Tensor> |
torch.fractional_max_pool2d_out(Tensor output,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples) |
static PointerPointer<Tensor> |
torch.fractional_max_pool2d_out(Tensor output,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples) |
static PointerPointer<Tensor> |
torch.fractional_max_pool2d_outf(Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static PointerPointer<Tensor> |
torch.fractional_max_pool2d_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static PointerPointer<Tensor> |
torch.fractional_max_pool3d_out(Tensor output,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples) |
static PointerPointer<Tensor> |
torch.fractional_max_pool3d_out(Tensor output,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples) |
static PointerPointer<Tensor> |
torch.fractional_max_pool3d_outf(Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static PointerPointer<Tensor> |
torch.fractional_max_pool3d_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static PointerPointer<Tensor> |
torch.frexp_out(Tensor mantissa,
Tensor exponent,
Tensor self) |
static PointerPointer<Tensor> |
torch.frexp_outf(Tensor self,
Tensor mantissa,
Tensor exponent) |
static PointerPointer<Tensor> |
torch.geqrf_out(Tensor a,
Tensor tau,
Tensor self) |
static PointerPointer<Tensor> |
torch.geqrf_outf(Tensor self,
Tensor a,
Tensor tau) |
static PointerPointer<Tensor> |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self) |
static PointerPointer<Tensor> |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self,
long bins,
DoubleArrayRefOptional range,
TensorOptional weight,
boolean density) |
static PointerPointer<Tensor> |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self,
Tensor bins) |
static PointerPointer<Tensor> |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self,
Tensor bins,
TensorOptional weight,
boolean density) |
static PointerPointer<Tensor> |
torch.histogram_outf(Tensor self,
long bins,
DoubleArrayRefOptional range,
TensorOptional weight,
boolean density,
Tensor hist,
Tensor bin_edges) |
static PointerPointer<Tensor> |
torch.histogram_outf(Tensor self,
Tensor bins,
TensorOptional weight,
boolean density,
Tensor hist,
Tensor bin_edges) |
static PointerPointer<Tensor> |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k) |
static PointerPointer<Tensor> |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k,
Dimname dim) |
static PointerPointer<Tensor> |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.kthvalue_outf(Tensor self,
long k,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.kthvalue_outf(Tensor self,
long k,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.linalg_cholesky_ex_out(Tensor L,
Tensor info,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_cholesky_ex_out(Tensor L,
Tensor info,
Tensor self,
boolean upper,
boolean check_errors) |
static PointerPointer<Tensor> |
torch.linalg_cholesky_ex_outf(Tensor self,
boolean upper,
boolean check_errors,
Tensor L,
Tensor info) |
static PointerPointer<Tensor> |
torch.linalg_eig_out(Tensor eigenvalues,
Tensor eigenvectors,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_eig_outf(Tensor self,
Tensor eigenvalues,
Tensor eigenvectors) |
static PointerPointer<Tensor> |
torch.linalg_eigh_out(Tensor eigvals,
Tensor eigvecs,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_eigh_out(Tensor eigvals,
Tensor eigvecs,
Tensor self,
Pointer UPLO) |
static PointerPointer<Tensor> |
torch.linalg_eigh_outf(Tensor self,
Pointer UPLO,
Tensor eigvals,
Tensor eigvecs) |
static PointerPointer<Tensor> |
torch.linalg_inv_ex_out(Tensor inverse,
Tensor info,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_inv_ex_out(Tensor inverse,
Tensor info,
Tensor self,
boolean check_errors) |
static PointerPointer<Tensor> |
torch.linalg_inv_ex_outf(Tensor self,
boolean check_errors,
Tensor inverse,
Tensor info) |
static PointerPointer<Tensor> |
torch.linalg_lstsq_out(Tensor solution,
Tensor residuals,
Tensor rank,
Tensor singular_values,
Tensor self,
Tensor b) |
static PointerPointer<Tensor> |
torch.linalg_lstsq_out(Tensor solution,
Tensor residuals,
Tensor rank,
Tensor singular_values,
Tensor self,
Tensor b,
DoubleOptional rcond,
Pointer driver) |
static PointerPointer<Tensor> |
torch.linalg_lstsq_outf(Tensor self,
Tensor b,
DoubleOptional rcond,
Pointer driver,
Tensor solution,
Tensor residuals,
Tensor rank,
Tensor singular_values) |
static PointerPointer<Tensor> |
torch.linalg_qr_out(Tensor Q,
Tensor R,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_qr_out(Tensor Q,
Tensor R,
Tensor self,
Pointer mode) |
static PointerPointer<Tensor> |
torch.linalg_qr_outf(Tensor self,
Pointer mode,
Tensor Q,
Tensor R) |
static PointerPointer<Tensor> |
torch.linalg_slogdet_out(Tensor sign,
Tensor logabsdet,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_slogdet_outf(Tensor self,
Tensor sign,
Tensor logabsdet) |
static PointerPointer<Tensor> |
torch.linalg_svd_out(Tensor U,
Tensor S,
Tensor Vh,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_svd_out(Tensor U,
Tensor S,
Tensor Vh,
Tensor self,
boolean full_matrices) |
static PointerPointer<Tensor> |
torch.linalg_svd_outf(Tensor self,
boolean full_matrices,
Tensor U,
Tensor S,
Tensor Vh) |
static PointerPointer<Tensor> |
torch.log_sigmoid_forward_out(Tensor output,
Tensor buffer,
Tensor self) |
static PointerPointer<Tensor> |
torch.log_sigmoid_forward_outf(Tensor self,
Tensor output,
Tensor buffer) |
static PointerPointer<Tensor> |
torch.lstsq_out(Tensor X,
Tensor qr,
Tensor self,
Tensor A) |
static PointerPointer<Tensor> |
torch.lstsq_outf(Tensor self,
Tensor A,
Tensor X,
Tensor qr) |
static PointerPointer<Tensor> |
torch.lu_unpack_out(Tensor P,
Tensor L,
Tensor U,
Tensor LU_data,
Tensor LU_pivots) |
static PointerPointer<Tensor> |
torch.lu_unpack_out(Tensor P,
Tensor L,
Tensor U,
Tensor LU_data,
Tensor LU_pivots,
boolean unpack_data,
boolean unpack_pivots) |
static PointerPointer<Tensor> |
torch.lu_unpack_outf(Tensor LU_data,
Tensor LU_pivots,
boolean unpack_data,
boolean unpack_pivots,
Tensor P,
Tensor L,
Tensor U) |
static PointerPointer<Tensor> |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.max_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor max,
Tensor max_values) |
static PointerPointer<Tensor> |
torch.max_outf(Tensor self,
long dim,
boolean keepdim,
Tensor max,
Tensor max_values) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long... kernel_size) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_outf(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long... kernel_size) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_outf(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.median_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.median_outf(Tensor self,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.min_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor min,
Tensor min_indices) |
static PointerPointer<Tensor> |
torch.min_outf(Tensor self,
long dim,
boolean keepdim,
Tensor min,
Tensor min_indices) |
static PointerPointer<Tensor> |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self) |
static PointerPointer<Tensor> |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.mode_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.mode_outf(Tensor self,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.multilabel_margin_loss_forward_out(Tensor output,
Tensor is_target,
Tensor self,
Tensor target,
long reduction) |
static PointerPointer<Tensor> |
torch.multilabel_margin_loss_forward_outf(Tensor self,
Tensor target,
long reduction,
Tensor output,
Tensor is_target) |
static PointerPointer<Tensor> |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.nanmedian_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.nanmedian_outf(Tensor self,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.native_batch_norm_out(Tensor out,
Tensor save_mean,
Tensor save_invstd,
Tensor input,
TensorOptional weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean training,
double momentum,
double eps) |
static PointerPointer<Tensor> |
torch.native_batch_norm_outf(Tensor input,
TensorOptional weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean training,
double momentum,
double eps,
Tensor out,
Tensor save_mean,
Tensor save_invstd) |
static PointerPointer<Tensor> |
torch.nll_loss_forward_out(Tensor output,
Tensor total_weight,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static PointerPointer<Tensor> |
torch.nll_loss_forward_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor output,
Tensor total_weight) |
static PointerPointer<Tensor> |
torch.nll_loss2d_forward_out(Tensor output,
Tensor total_weight,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static PointerPointer<Tensor> |
torch.nll_loss2d_forward_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor output,
Tensor total_weight) |
static PointerPointer<Tensor> |
torch.qr_out(Tensor Q,
Tensor R,
Tensor self) |
static PointerPointer<Tensor> |
torch.qr_out(Tensor Q,
Tensor R,
Tensor self,
boolean some) |
static PointerPointer<Tensor> |
torch.qr_outf(Tensor self,
boolean some,
Tensor Q,
Tensor R) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor columns,
Tensor ones) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor columns,
Tensor ones) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor columns,
Tensor ones,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor columns,
Tensor ones,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor finput,
Tensor fgrad_input,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor finput,
Tensor fgrad_input,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.slow_conv3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.slow_conv3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.slow_conv3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
Tensor finput,
Tensor fgrad_input,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.slow_conv3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor finput,
Tensor fgrad_input,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.slow_conv3d_forward_out(Tensor output,
Tensor finput,
Tensor fgrad_input,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static PointerPointer<Tensor> |
torch.slow_conv3d_forward_out(Tensor output,
Tensor finput,
Tensor fgrad_input,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static PointerPointer<Tensor> |
torch.slow_conv3d_forward_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
Tensor output,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.slow_conv3d_forward_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
Tensor output,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.solve_out(Tensor solution,
Tensor lu,
Tensor self,
Tensor A) |
static PointerPointer<Tensor> |
torch.solve_outf(Tensor self,
Tensor A,
Tensor solution,
Tensor lu) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable,
Dimname dim) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable,
Dimname dim,
boolean descending) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable,
long dim,
boolean descending) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean descending) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean descending) |
static PointerPointer<Tensor> |
torch.sort_outf(Tensor self,
BoolOptional stable,
Dimname dim,
boolean descending,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.sort_outf(Tensor self,
BoolOptional stable,
long dim,
boolean descending,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.sort_outf(Tensor self,
Dimname dim,
boolean descending,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.sort_outf(Tensor self,
long dim,
boolean descending,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.svd_out(Tensor U,
Tensor S,
Tensor V,
Tensor self) |
static PointerPointer<Tensor> |
torch.svd_out(Tensor U,
Tensor S,
Tensor V,
Tensor self,
boolean some,
boolean compute_uv) |
static PointerPointer<Tensor> |
torch.svd_outf(Tensor self,
boolean some,
boolean compute_uv,
Tensor U,
Tensor S,
Tensor V) |
static PointerPointer<Tensor> |
torch.symeig_out(Tensor e,
Tensor V,
Tensor self) |
static PointerPointer<Tensor> |
torch.symeig_out(Tensor e,
Tensor V,
Tensor self,
boolean eigenvectors,
boolean upper) |
static PointerPointer<Tensor> |
torch.symeig_outf(Tensor self,
boolean eigenvectors,
boolean upper,
Tensor e,
Tensor V) |
static PointerPointer<Tensor> |
torch.topk_out(Tensor values,
Tensor indices,
Tensor self,
long k) |
static PointerPointer<Tensor> |
torch.topk_out(Tensor values,
Tensor indices,
Tensor self,
long k,
long dim,
boolean largest,
boolean sorted) |
static PointerPointer<Tensor> |
torch.topk_outf(Tensor self,
long k,
long dim,
boolean largest,
boolean sorted,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.triangular_solve_out(Tensor X,
Tensor M,
Tensor self,
Tensor A) |
static PointerPointer<Tensor> |
torch.triangular_solve_out(Tensor X,
Tensor M,
Tensor self,
Tensor A,
boolean upper,
boolean transpose,
boolean unitriangular) |
static PointerPointer<Tensor> |
torch.triangular_solve_outf(Tensor self,
Tensor A,
boolean upper,
boolean transpose,
boolean unitriangular,
Tensor X,
Tensor M) |
| Modifier and Type | Method and Description |
|---|---|
static Tensor |
torch.__and__(Tensor self,
Scalar other) |
static Tensor |
torch.__and__(Tensor self,
Tensor other) |
static Tensor |
torch.__dispatch_conj(Tensor self) |
static boolean |
torch.__dispatch_is_complex(Tensor self) |
static boolean |
torch.__dispatch_is_conj(Tensor self) |
static boolean |
torch.__dispatch_is_floating_point(Tensor self) |
static boolean |
torch.__dispatch_is_inference(Tensor self) |
static boolean |
torch.__dispatch_is_neg(Tensor self) |
static boolean |
torch.__dispatch_is_signed(Tensor self) |
static long |
torch.__dispatch_size(Tensor self,
long dim) |
static long |
torch.__dispatch_stride(Tensor self,
long dim) |
static Tensor |
torch.__lshift__(Tensor self,
Scalar other) |
static Tensor |
torch.__lshift__(Tensor self,
Tensor other) |
static Tensor |
torch.__or__(Tensor self,
Scalar other) |
static Tensor |
torch.__or__(Tensor self,
Tensor other) |
static Tensor |
torch.__rshift__(Tensor self,
Scalar other) |
static Tensor |
torch.__rshift__(Tensor self,
Tensor other) |
static Tensor |
torch.__xor__(Tensor self,
Scalar other) |
static Tensor |
torch.__xor__(Tensor self,
Tensor other) |
static Tensor |
torch._adaptive_avg_pool2d_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch._adaptive_avg_pool2d(Tensor self,
long... output_size) |
static Tensor |
torch._adaptive_avg_pool2d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch._adaptive_avg_pool3d_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch._adaptive_avg_pool3d(Tensor self,
long... output_size) |
static Tensor |
torch._adaptive_avg_pool3d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch._add_batch_dim(Tensor self,
long batch_dim,
long level) |
static Tensor |
torch._add_relu_(Tensor self,
Scalar other) |
static Tensor |
torch._add_relu_(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch._add_relu_(Tensor self,
Tensor other) |
static Tensor |
torch._add_relu_(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch._add_relu_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch._add_relu_out(Tensor out,
Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch._add_relu_outf(Tensor self,
Tensor other,
Scalar alpha,
Tensor out) |
static Tensor |
torch._add_relu(Tensor self,
Scalar other) |
static Tensor |
torch._add_relu(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch._add_relu(Tensor self,
Tensor other) |
static Tensor |
torch._add_relu(Tensor self,
Tensor other,
Scalar alpha) |
static TensorTensorTuple |
torch._aminmax(Tensor self) |
static TensorTensorTuple |
torch._aminmax(Tensor self,
long dim) |
static TensorTensorTuple |
torch._aminmax(Tensor self,
long dim,
boolean keepdim) |
static void |
torch._amp_foreach_non_finite_check_and_unscale_(TensorArrayRef self,
Tensor found_inf,
Tensor inv_scale) |
static Tensor |
torch._amp_update_scale_(Tensor self,
Tensor growth_tracker,
Tensor found_inf,
double scale_growth_factor,
double scale_backoff_factor,
long growth_interval) |
static void |
torch._assert_async(Tensor self) |
static Tensor |
torch._baddbmm_mkl_(Tensor self,
Tensor batch1,
Tensor batch2) |
static Tensor |
torch._baddbmm_mkl_(Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
static TensorTensorTensorTuple |
torch._batch_norm_impl_index_backward(long impl_index,
Tensor input,
Tensor grad_output,
TensorOptional weight,
TensorOptional running_mean,
TensorOptional running_var,
TensorOptional save_mean,
TensorOptional save_var_transform,
boolean train,
double eps,
BoolPointer output_mask,
Tensor reservedSpace) |
static TensorTensorTensorTensorLongTuple |
torch._batch_norm_impl_index(Tensor input,
TensorOptional weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean training,
double momentum,
double eps,
boolean cudnn_enabled) |
static LongPointer |
torch._calculate_fan_in_and_fan_out(Tensor tensor) |
static Tensor |
torch._cast_Byte(Tensor self) |
static Tensor |
torch._cast_Byte(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Char(Tensor self) |
static Tensor |
torch._cast_Char(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Double(Tensor self) |
static Tensor |
torch._cast_Double(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Float(Tensor self) |
static Tensor |
torch._cast_Float(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Half(Tensor self) |
static Tensor |
torch._cast_Half(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Int(Tensor self) |
static Tensor |
torch._cast_Int(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Long(Tensor self) |
static Tensor |
torch._cast_Long(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cast_Short(Tensor self) |
static Tensor |
torch._cast_Short(Tensor self,
boolean non_blocking) |
static Tensor |
torch._cat_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch._cat_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch._cat_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch._cdist_backward(Tensor grad,
Tensor x1,
Tensor x2,
double p,
Tensor cdist) |
static Tensor |
torch._cdist_forward(Tensor x1,
Tensor x2,
double p,
LongOptional compute_mode) |
static LongOptional |
torch._check_param_device(Tensor param,
LongOptional old_param_device) |
static Tensor |
torch._cholesky_solve_helper(Tensor self,
Tensor A,
boolean upper) |
static LongPointer |
torch._choose_qparams_per_tensor(Tensor self) |
static LongPointer |
torch._choose_qparams_per_tensor(Tensor self,
boolean reduce_range) |
static Tensor |
torch._coalesce(Tensor self) |
static Tensor |
torch._compute_linear_combination_out(Tensor out,
Tensor input,
Tensor coefficients) |
static Tensor |
torch._compute_linear_combination_outf(Tensor input,
Tensor coefficients,
Tensor out) |
static Tensor |
torch._compute_linear_combination(Tensor input,
Tensor coefficients) |
static Tensor |
torch._conj_physical(Tensor self) |
static Tensor |
torch._conj(Tensor self) |
static PointerPointer<Tensor> |
torch._conv_depthwise2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long... dilation) |
static PointerPointer<Tensor> |
torch._conv_depthwise2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static PointerPointer<Tensor> |
torch._conv_depthwise2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
Tensor grad_input,
Tensor grad_weight) |
static PointerPointer<Tensor> |
torch._conv_depthwise2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
Tensor grad_input,
Tensor grad_weight) |
static TensorTensorTuple |
torch._conv_depthwise2d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
BoolPointer output_mask) |
static TensorTensorTuple |
torch._conv_depthwise2d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
BoolPointer output_mask) |
static Tensor |
torch._conv_depthwise2d_out(Tensor out,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long... dilation) |
static Tensor |
torch._conv_depthwise2d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static Tensor |
torch._conv_depthwise2d_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
Tensor out) |
static Tensor |
torch._conv_depthwise2d_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
Tensor out) |
static Tensor |
torch._conv_depthwise2d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long... dilation) |
static Tensor |
torch._conv_depthwise2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static Tensor |
torch._convert_indices_from_coo_to_csr_out(Tensor out,
Tensor self,
long size) |
static Tensor |
torch._convert_indices_from_coo_to_csr_out(Tensor out,
Tensor self,
long size,
boolean out_int32) |
static Tensor |
torch._convert_indices_from_coo_to_csr_outf(Tensor self,
long size,
boolean out_int32,
Tensor out) |
static Tensor |
torch._convert_indices_from_coo_to_csr(Tensor self,
long size) |
static Tensor |
torch._convert_indices_from_coo_to_csr(Tensor self,
long size,
boolean out_int32) |
static TensorTensorTensorTuple |
torch._convolution_double_backward(TensorOptional ggI,
TensorOptional ggW,
TensorOptional ggb,
Tensor gO,
Tensor weight,
Tensor self,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long[] output_padding,
long groups,
boolean benchmark,
boolean deterministic,
boolean cudnn_enabled,
boolean allow_tf32,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch._convolution_double_backward(TensorOptional ggI,
TensorOptional ggW,
TensorOptional ggb,
Tensor gO,
Tensor weight,
Tensor self,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding,
long groups,
boolean benchmark,
boolean deterministic,
boolean cudnn_enabled,
boolean allow_tf32,
BoolPointer output_mask) |
static Tensor |
torch._convolution_mode(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding,
long[] dilation,
long groups) |
static Tensor |
torch._convolution_mode(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch._convolution_nogroup(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long... output_padding) |
static Tensor |
torch._convolution_nogroup(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding) |
static Tensor |
torch._convolution(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long[] output_padding,
long groups,
boolean benchmark,
boolean deterministic,
boolean cudnn_enabled) |
static Tensor |
torch._convolution(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long[] output_padding,
long groups,
boolean benchmark,
boolean deterministic,
boolean cudnn_enabled,
boolean allow_tf32) |
static Tensor |
torch._convolution(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding,
long groups,
boolean benchmark,
boolean deterministic,
boolean cudnn_enabled) |
static Tensor |
torch._convolution(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding,
long groups,
boolean benchmark,
boolean deterministic,
boolean cudnn_enabled,
boolean allow_tf32) |
static Tensor |
torch._copy_from_and_resize(Tensor self,
Tensor dst) |
static Tensor |
torch._copy_from(Tensor self,
Tensor dst) |
static Tensor |
torch._copy_from(Tensor self,
Tensor dst,
boolean non_blocking) |
static Tensor |
torch._ctc_loss_backward(Tensor grad,
Tensor log_probs,
Tensor targets,
long[] input_lengths,
long[] target_lengths,
Tensor neg_log_likelihood,
Tensor log_alpha,
long blank) |
static Tensor |
torch._ctc_loss_backward(Tensor grad,
Tensor log_probs,
Tensor targets,
long[] input_lengths,
long[] target_lengths,
Tensor neg_log_likelihood,
Tensor log_alpha,
long blank,
boolean zero_infinity) |
static Tensor |
torch._ctc_loss_backward(Tensor grad,
Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths,
Tensor neg_log_likelihood,
Tensor log_alpha,
long blank) |
static Tensor |
torch._ctc_loss_backward(Tensor grad,
Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths,
Tensor neg_log_likelihood,
Tensor log_alpha,
long blank,
boolean zero_infinity) |
static TensorTensorTuple |
torch._ctc_loss(Tensor log_probs,
Tensor targets,
long[] input_lengths,
long... target_lengths) |
static TensorTensorTuple |
torch._ctc_loss(Tensor log_probs,
Tensor targets,
long[] input_lengths,
long[] target_lengths,
long blank,
boolean zero_infinity) |
static TensorTensorTuple |
torch._ctc_loss(Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths) |
static TensorTensorTuple |
torch._ctc_loss(Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths,
long blank,
boolean zero_infinity) |
static TensorTensorTuple |
torch._cudnn_ctc_loss(Tensor log_probs,
Tensor targets,
long[] input_lengths,
long[] target_lengths,
long blank,
boolean deterministic,
boolean zero_infinity) |
static TensorTensorTuple |
torch._cudnn_ctc_loss(Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths,
long blank,
boolean deterministic,
boolean zero_infinity) |
static TensorTensorTensorTensorVectorTuple |
torch._cudnn_rnn_backward(Tensor input,
TensorArrayRef weight,
long weight_stride0,
Tensor weight_buf,
Tensor hx,
TensorOptional cx,
Tensor output,
TensorOptional grad_output,
TensorOptional grad_hy,
TensorOptional grad_cy,
long mode,
long hidden_size,
long proj_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
long[] batch_sizes,
TensorOptional dropout_state,
Tensor reserve,
BoolPointer output_mask) |
static TensorTensorTensorTensorVectorTuple |
torch._cudnn_rnn_backward(Tensor input,
TensorArrayRef weight,
long weight_stride0,
Tensor weight_buf,
Tensor hx,
TensorOptional cx,
Tensor output,
TensorOptional grad_output,
TensorOptional grad_hy,
TensorOptional grad_cy,
long mode,
long hidden_size,
long proj_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
LongArrayRef batch_sizes,
TensorOptional dropout_state,
Tensor reserve,
BoolPointer output_mask) |
static TensorTensorTensorTensorTensorTuple |
torch._cudnn_rnn(Tensor input,
TensorArrayRef weight,
long weight_stride0,
TensorOptional weight_buf,
Tensor hx,
TensorOptional cx,
long mode,
long hidden_size,
long proj_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
long[] batch_sizes,
TensorOptional dropout_state) |
static TensorTensorTensorTensorTensorTuple |
torch._cudnn_rnn(Tensor input,
TensorArrayRef weight,
long weight_stride0,
TensorOptional weight_buf,
Tensor hx,
TensorOptional cx,
long mode,
long hidden_size,
long proj_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
LongArrayRef batch_sizes,
TensorOptional dropout_state) |
static void |
torch._cummax_helper(Tensor self,
Tensor values,
Tensor indices,
long dim) |
static void |
torch._cummin_helper(Tensor self,
Tensor values,
Tensor indices,
long dim) |
static long |
torch._debug_has_internal_overlap(Tensor self) |
static Tensor |
torch._det_lu_based_helper_backward_helper(Tensor det_grad,
Tensor det,
Tensor self,
Tensor lu,
Tensor pivs) |
static TensorTensorTensorTuple |
torch._det_lu_based_helper(Tensor self) |
static Tensor |
torch._dim_arange(Tensor like,
long dim) |
static Tensor |
torch._dirichlet_grad(Tensor x,
Tensor alpha,
Tensor total) |
static Tensor |
torch._embedding_bag_backward(Tensor grad,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
Tensor bag_size,
Tensor maximum_indices,
long num_weights,
boolean scale_grad_by_freq,
long mode,
boolean sparse,
TensorOptional per_sample_weights) |
static Tensor |
torch._embedding_bag_backward(Tensor grad,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
Tensor bag_size,
Tensor maximum_indices,
long num_weights,
boolean scale_grad_by_freq,
long mode,
boolean sparse,
TensorOptional per_sample_weights,
long padding_idx) |
static Tensor |
torch._embedding_bag_dense_backward(Tensor grad,
Tensor indices,
Tensor offset2bag,
Tensor bag_size,
Tensor maximum_indices,
long num_weights,
boolean scale_grad_by_freq,
long mode,
TensorOptional per_sample_weights) |
static Tensor |
torch._embedding_bag_dense_backward(Tensor grad,
Tensor indices,
Tensor offset2bag,
Tensor bag_size,
Tensor maximum_indices,
long num_weights,
boolean scale_grad_by_freq,
long mode,
TensorOptional per_sample_weights,
long padding_idx) |
static TensorTensorTensorTensorTuple |
torch._embedding_bag_forward_only(Tensor weight,
Tensor indices,
Tensor offsets) |
static TensorTensorTensorTensorTuple |
torch._embedding_bag_forward_only(Tensor weight,
Tensor indices,
Tensor offsets,
boolean scale_grad_by_freq,
long mode,
boolean sparse,
TensorOptional per_sample_weights,
boolean include_last_offset,
long padding_idx) |
static Tensor |
torch._embedding_bag_per_sample_weights_backward(Tensor grad,
Tensor weight,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
long mode) |
static Tensor |
torch._embedding_bag_per_sample_weights_backward(Tensor grad,
Tensor weight,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
long mode,
long padding_idx) |
static Tensor |
torch._embedding_bag_sparse_backward(Tensor grad,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
Tensor bag_size,
long num_weights,
boolean scale_grad_by_freq,
long mode,
TensorOptional per_sample_weights) |
static Tensor |
torch._embedding_bag_sparse_backward(Tensor grad,
Tensor indices,
Tensor offsets,
Tensor offset2bag,
Tensor bag_size,
long num_weights,
boolean scale_grad_by_freq,
long mode,
TensorOptional per_sample_weights,
long padding_idx) |
static TensorTensorTensorTensorTuple |
torch._embedding_bag(Tensor weight,
Tensor indices,
Tensor offsets) |
static TensorTensorTensorTensorTuple |
torch._embedding_bag(Tensor weight,
Tensor indices,
Tensor offsets,
boolean scale_grad_by_freq,
long mode,
boolean sparse,
TensorOptional per_sample_weights,
boolean include_last_offset,
long padding_idx) |
static Tensor |
torch._empty_per_channel_affine_quantized(long[] size,
Tensor scales,
Tensor zero_points,
long axis) |
static Tensor |
torch._empty_per_channel_affine_quantized(long[] size,
Tensor scales,
Tensor zero_points,
long axis,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch._empty_per_channel_affine_quantized(long[] size,
Tensor scales,
Tensor zero_points,
long axis,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch._empty_per_channel_affine_quantized(LongArrayRef size,
Tensor scales,
Tensor zero_points,
long axis) |
static Tensor |
torch._empty_per_channel_affine_quantized(LongArrayRef size,
Tensor scales,
Tensor zero_points,
long axis,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch._empty_per_channel_affine_quantized(LongArrayRef size,
Tensor scales,
Tensor zero_points,
long axis,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch._euclidean_dist(Tensor x1,
Tensor x2) |
static TensorTensorTensorTuple |
torch._fake_quantize_learnable_per_channel_affine_backward(Tensor grad,
Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max) |
static TensorTensorTensorTuple |
torch._fake_quantize_learnable_per_channel_affine_backward(Tensor grad,
Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max,
double grad_factor) |
static Tensor |
torch._fake_quantize_learnable_per_channel_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max) |
static Tensor |
torch._fake_quantize_learnable_per_channel_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max,
double grad_factor) |
static TensorTensorTensorTuple |
torch._fake_quantize_learnable_per_tensor_affine_backward(Tensor grad,
Tensor self,
Tensor scale,
Tensor zero_point,
long quant_min,
long quant_max) |
static TensorTensorTensorTuple |
torch._fake_quantize_learnable_per_tensor_affine_backward(Tensor grad,
Tensor self,
Tensor scale,
Tensor zero_point,
long quant_min,
long quant_max,
double grad_factor) |
static Tensor |
torch._fake_quantize_learnable_per_tensor_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long quant_min,
long quant_max) |
static Tensor |
torch._fake_quantize_learnable_per_tensor_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long quant_min,
long quant_max,
double grad_factor) |
static TensorTensorTuple |
torch._fake_quantize_per_tensor_affine_cachemask_tensor_qparams(Tensor self,
Tensor scale,
Tensor zero_point,
Tensor fake_quant_enabled,
long quant_min,
long quant_max) |
static Tensor |
torch._fft_c2c_out(Tensor out,
Tensor self,
long[] dim,
long normalization,
boolean forward) |
static Tensor |
torch._fft_c2c_out(Tensor out,
Tensor self,
LongArrayRef dim,
long normalization,
boolean forward) |
static Tensor |
torch._fft_c2c_outf(Tensor self,
long[] dim,
long normalization,
boolean forward,
Tensor out) |
static Tensor |
torch._fft_c2c_outf(Tensor self,
LongArrayRef dim,
long normalization,
boolean forward,
Tensor out) |
static Tensor |
torch._fft_c2c(Tensor self,
long[] dim,
long normalization,
boolean forward) |
static Tensor |
torch._fft_c2c(Tensor self,
LongArrayRef dim,
long normalization,
boolean forward) |
static Tensor |
torch._fft_c2r_out(Tensor out,
Tensor self,
long[] dim,
long normalization,
long last_dim_size) |
static Tensor |
torch._fft_c2r_out(Tensor out,
Tensor self,
LongArrayRef dim,
long normalization,
long last_dim_size) |
static Tensor |
torch._fft_c2r_outf(Tensor self,
long[] dim,
long normalization,
long last_dim_size,
Tensor out) |
static Tensor |
torch._fft_c2r_outf(Tensor self,
LongArrayRef dim,
long normalization,
long last_dim_size,
Tensor out) |
static Tensor |
torch._fft_c2r(Tensor self,
long[] dim,
long normalization,
long last_dim_size) |
static Tensor |
torch._fft_c2r(Tensor self,
LongArrayRef dim,
long normalization,
long last_dim_size) |
static Tensor |
torch._fft_r2c_out(Tensor out,
Tensor self,
long[] dim,
long normalization,
boolean onesided) |
static Tensor |
torch._fft_r2c_out(Tensor out,
Tensor self,
LongArrayRef dim,
long normalization,
boolean onesided) |
static Tensor |
torch._fft_r2c_outf(Tensor self,
long[] dim,
long normalization,
boolean onesided,
Tensor out) |
static Tensor |
torch._fft_r2c_outf(Tensor self,
LongArrayRef dim,
long normalization,
boolean onesided,
Tensor out) |
static Tensor |
torch._fft_r2c(Tensor self,
long[] dim,
long normalization,
boolean onesided) |
static Tensor |
torch._fft_r2c(Tensor self,
LongArrayRef dim,
long normalization,
boolean onesided) |
static TensorTensorTuple |
torch._fused_dropout(Tensor self,
double p) |
static TensorTensorTuple |
torch._fused_dropout(Tensor self,
double p,
GeneratorOptional generator) |
static TensorTensorTuple |
torch._fused_moving_avg_obs_fq_helper(Tensor self,
Tensor observer_on,
Tensor fake_quant_on,
Tensor running_min,
Tensor running_max,
Tensor scale,
Tensor zero_point,
double averaging_const,
long quant_min,
long quant_max,
long ch_axis) |
static TensorTensorTuple |
torch._fused_moving_avg_obs_fq_helper(Tensor self,
Tensor observer_on,
Tensor fake_quant_on,
Tensor running_min,
Tensor running_max,
Tensor scale,
Tensor zero_point,
double averaging_const,
long quant_min,
long quant_max,
long ch_axis,
boolean per_row_fake_quant,
boolean symmetric_quant) |
static Tensor |
torch._gather_sparse_backward(Tensor self,
long dim,
Tensor index,
Tensor grad) |
static TensorTensorTuple |
torch._grid_sampler_2d_cpu_fallback_backward(Tensor grad_output,
Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static Tensor |
torch._grid_sampler_2d_cpu_fallback(Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static boolean |
torch._has_compatible_shallow_copy_type(Tensor self,
Tensor from) |
static Tensor |
torch._index_copy_(Tensor self,
long dim,
Tensor index,
Tensor source) |
static Tensor |
torch._inverse_helper(Tensor self) |
static Tensor |
torch._linalg_inv_out_helper_(Tensor self,
Tensor infos_lu,
Tensor infos_getri) |
static TensorTensorTuple |
torch._linalg_qr_helper(Tensor self,
Pointer mode) |
static Scalar |
torch._local_scalar_dense(Tensor self) |
static Tensor |
torch._log_softmax_backward_data_out(Tensor out,
Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._log_softmax_backward_data_outf(Tensor grad_output,
Tensor output,
long dim,
Tensor self,
Tensor out) |
static Tensor |
torch._log_softmax_backward_data(Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._log_softmax_out(Tensor out,
Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._log_softmax_outf(Tensor self,
long dim,
boolean half_to_float,
Tensor out) |
static Tensor |
torch._log_softmax(Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._logcumsumexp_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch._logcumsumexp_outf(Tensor self,
long dim,
Tensor out) |
static Tensor |
torch._logcumsumexp(Tensor self,
long dim) |
static TensorTensorTensorTuple |
torch._lu_with_info(Tensor self) |
static TensorTensorTensorTuple |
torch._lu_with_info(Tensor self,
boolean pivot,
boolean check_errors) |
static Tensor |
torch._make_dual(Tensor primal,
Tensor tangent,
long level) |
static Tensor |
torch._make_per_channel_quantized_tensor(Tensor self,
Tensor scale,
Tensor zero_point,
long axis) |
static Tensor |
torch._make_per_tensor_quantized_tensor(Tensor self,
double scale,
long zero_point) |
static Tensor |
torch._masked_scale(Tensor self,
Tensor mask,
double scale) |
static Tensor |
torch._mkldnn_reshape(Tensor self,
long... shape) |
static Tensor |
torch._mkldnn_reshape(Tensor self,
LongArrayRef shape) |
static Tensor |
torch._mkldnn_transpose_(Tensor self,
long dim0,
long dim1) |
static Tensor |
torch._mkldnn_transpose(Tensor self,
long dim0,
long dim1) |
static Tensor |
torch._narrow_with_range(Tensor input,
long dim,
long start,
long end) |
static Tensor |
torch._neg_view(Tensor self) |
static Tensor |
torch._nnpack_spatial_convolution_backward_input(Tensor input,
Tensor grad_output,
Tensor weight,
long... padding) |
static Tensor |
torch._nnpack_spatial_convolution_backward_input(Tensor input,
Tensor grad_output,
Tensor weight,
LongArrayRef padding) |
static Tensor |
torch._nnpack_spatial_convolution_backward_weight(Tensor input,
long[] weightsize,
Tensor grad_output,
long... padding) |
static Tensor |
torch._nnpack_spatial_convolution_backward_weight(Tensor input,
LongArrayRef weightsize,
Tensor grad_output,
LongArrayRef padding) |
static TensorTensorTensorTuple |
torch._nnpack_spatial_convolution_backward(Tensor input,
Tensor grad_output,
Tensor weight,
long[] padding,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch._nnpack_spatial_convolution_backward(Tensor input,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
BoolPointer output_mask) |
static Tensor |
torch._nnpack_spatial_convolution(Tensor input,
Tensor weight,
TensorOptional bias,
long... padding) |
static Tensor |
torch._nnpack_spatial_convolution(Tensor input,
Tensor weight,
TensorOptional bias,
long[] padding,
long... stride) |
static Tensor |
torch._nnpack_spatial_convolution(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef padding) |
static Tensor |
torch._nnpack_spatial_convolution(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef stride) |
static void |
torch._no_grad_embedding_renorm_(Tensor weight,
Tensor input,
float max_norm,
float norm_type) |
static Tensor |
torch._pack_padded_sequence_backward(Tensor grad,
long[] input_size,
Tensor batch_sizes,
boolean batch_first) |
static Tensor |
torch._pack_padded_sequence_backward(Tensor grad,
LongArrayRef input_size,
Tensor batch_sizes,
boolean batch_first) |
static TensorTensorTuple |
torch._pack_padded_sequence(Tensor input,
Tensor lengths,
boolean batch_first) |
static Tensor |
torch._pad_circular(Tensor input,
long... padding) |
static Tensor |
torch._pad_circular(Tensor input,
LongArrayRef padding) |
static TensorTensorTuple |
torch._pad_packed_sequence(Tensor data,
Tensor batch_sizes,
boolean batch_first,
Scalar padding_value,
long total_length) |
static Tensor |
torch._pdist_backward(Tensor grad,
Tensor self,
double p,
Tensor pdist) |
static Tensor |
torch._pdist_forward(Tensor self) |
static Tensor |
torch._pdist_forward(Tensor self,
double p) |
static Tensor |
torch._pin_memory(Tensor self) |
static Tensor |
torch._pin_memory(Tensor self,
DeviceOptional device) |
static Tensor |
torch._remove_batch_dim(Tensor self,
long level,
long batch_size,
long out_dim) |
static Tensor |
torch._reshape_alias(Tensor self,
long[] size,
long... stride) |
static Tensor |
torch._reshape_alias(Tensor self,
LongArrayRef size,
LongArrayRef stride) |
static Tensor |
torch._reshape_from_tensor(Tensor self,
Tensor shape) |
static TensorTensorTuple |
torch._rowwise_prune(Tensor weight,
Tensor mask,
torch.ScalarType compressed_indices_dtype) |
static Tensor |
torch._s_where(Tensor condition,
Tensor self,
Tensor other) |
static Tensor |
torch._sample_dirichlet(Tensor self) |
static Tensor |
torch._sample_dirichlet(Tensor self,
GeneratorOptional generator) |
static Tensor |
torch._saturate_weight_to_fp16(Tensor weight) |
static Tensor |
torch._segment_reduce_backward(Tensor grad,
Tensor output,
Tensor data,
Pointer reduce) |
static Tensor |
torch._segment_reduce_backward(Tensor grad,
Tensor output,
Tensor data,
Pointer reduce,
TensorOptional lengths,
long axis) |
static Tensor |
torch._shape_as_tensor(Tensor self) |
static PointerPointer<Tensor> |
torch._slow_conv2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
Tensor finput) |
static PointerPointer<Tensor> |
torch._slow_conv2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor finput) |
static PointerPointer<Tensor> |
torch._slow_conv2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
Tensor finput,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch._slow_conv2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor finput,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static TensorTensorTensorTuple |
torch._slow_conv2d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
Tensor finput,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch._slow_conv2d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor finput,
BoolPointer output_mask) |
static PointerPointer<Tensor> |
torch._slow_conv2d_forward_out(Tensor output,
Tensor finput,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static PointerPointer<Tensor> |
torch._slow_conv2d_forward_out(Tensor output,
Tensor finput,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static PointerPointer<Tensor> |
torch._slow_conv2d_forward_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
Tensor output,
Tensor finput) |
static PointerPointer<Tensor> |
torch._slow_conv2d_forward_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
Tensor output,
Tensor finput) |
static TensorTensorTuple |
torch._slow_conv2d_forward(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static TensorTensorTuple |
torch._slow_conv2d_forward(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch._smooth_l1_loss(Tensor input,
Tensor target) |
static Tensor |
torch._smooth_l1_loss(Tensor input,
Tensor target,
double beta) |
static TensorTensorTuple |
torch._sobol_engine_draw(Tensor quasi,
long n,
Tensor sobolstate,
long dimension,
long num_generated,
ScalarTypeOptional dtype) |
static Tensor |
torch._sobol_engine_ff_(Tensor self,
long n,
Tensor sobolstate,
long dimension,
long num_generated) |
static Tensor |
torch._sobol_engine_initialize_state_(Tensor self,
long dimension) |
static Tensor |
torch._sobol_engine_scramble_(Tensor self,
Tensor ltm,
long dimension) |
static Tensor |
torch._softmax_backward_data_out(Tensor grad_input,
Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._softmax_backward_data_outf(Tensor grad_output,
Tensor output,
long dim,
Tensor self,
Tensor grad_input) |
static Tensor |
torch._softmax_backward_data(Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._softmax_out(Tensor out,
Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._softmax_outf(Tensor self,
long dim,
boolean half_to_float,
Tensor out) |
static Tensor |
torch._softmax(Tensor self,
long dim,
boolean half_to_float) |
static TensorTensorTuple |
torch._solve_helper(Tensor self,
Tensor A) |
static Tensor |
torch._sparse_addmm(Tensor self,
Tensor sparse,
Tensor dense) |
static Tensor |
torch._sparse_addmm(Tensor self,
Tensor sparse,
Tensor dense,
Scalar beta,
Scalar alpha) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
long... size) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
long[] size,
TensorOptions options) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
LongArrayRef size) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_coo_tensor_unsafe(Tensor indices,
Tensor values,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch._sparse_coo_tensor_with_dims_and_tensors(long sparse_dim,
long dense_dim,
long[] size,
Tensor indices,
Tensor values,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_coo_tensor_with_dims_and_tensors(long sparse_dim,
long dense_dim,
long[] size,
Tensor indices,
Tensor values,
TensorOptions options) |
static Tensor |
torch._sparse_coo_tensor_with_dims_and_tensors(long sparse_dim,
long dense_dim,
LongArrayRef size,
Tensor indices,
Tensor values,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_coo_tensor_with_dims_and_tensors(long sparse_dim,
long dense_dim,
LongArrayRef size,
Tensor indices,
Tensor values,
TensorOptions options) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long... size) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long[] size,
TensorOptions options) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch._sparse_csr_tensor_unsafe(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch._sparse_log_softmax_backward_data(Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._sparse_log_softmax(Tensor self,
Dimname dim) |
static Tensor |
torch._sparse_log_softmax(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch._sparse_log_softmax(Tensor self,
long dim) |
static Tensor |
torch._sparse_log_softmax(Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._sparse_log_softmax(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch._sparse_mask_helper(Tensor t,
Tensor mask_indices) |
static Tensor |
torch._sparse_mm(Tensor sparse,
Tensor dense) |
static Tensor |
torch._sparse_softmax_backward_data(Tensor grad_output,
Tensor output,
long dim,
Tensor self) |
static Tensor |
torch._sparse_softmax(Tensor self,
Dimname dim) |
static Tensor |
torch._sparse_softmax(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch._sparse_softmax(Tensor self,
long dim) |
static Tensor |
torch._sparse_softmax(Tensor self,
long dim,
boolean half_to_float) |
static Tensor |
torch._sparse_softmax(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch._sparse_sparse_matmul(Tensor self,
Tensor other) |
static Tensor |
torch._sparse_sum_backward(Tensor grad,
Tensor self,
long... dim) |
static Tensor |
torch._sparse_sum_backward(Tensor grad,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch._sparse_sum(Tensor self) |
static Tensor |
torch._sparse_sum(Tensor self,
long... dim) |
static Tensor |
torch._sparse_sum(Tensor self,
long[] dim,
torch.ScalarType dtype) |
static Tensor |
torch._sparse_sum(Tensor self,
LongArrayRef dim) |
static Tensor |
torch._sparse_sum(Tensor self,
LongArrayRef dim,
torch.ScalarType dtype) |
static Tensor |
torch._sparse_sum(Tensor self,
torch.ScalarType dtype) |
static Tensor |
torch._stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch._stack_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch._stack_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch._standard_gamma_grad(Tensor self,
Tensor output) |
static Tensor |
torch._standard_gamma(Tensor self) |
static Tensor |
torch._standard_gamma(Tensor self,
GeneratorOptional generator) |
static TensorTensorTensorTuple |
torch._svd_helper(Tensor self,
boolean some,
boolean compute_uv) |
static TensorTensorTuple |
torch._symeig_helper(Tensor self,
boolean eigenvectors,
boolean upper) |
static TensorTensorTensorTensorTensorTuple |
torch._thnn_differentiable_gru_cell_backward(Tensor grad_hy,
Tensor input_gates,
Tensor hidden_gates,
Tensor hx,
TensorOptional input_bias,
TensorOptional hidden_bias) |
static TensorTensorTensorTensorTensorTuple |
torch._thnn_differentiable_lstm_cell_backward(TensorOptional grad_hy,
TensorOptional grad_cy,
Tensor input_gates,
Tensor hidden_gates,
TensorOptional input_bias,
TensorOptional hidden_bias,
Tensor cx,
Tensor cy) |
static TensorTensorTensorTensorTensorTuple |
torch._thnn_fused_gru_cell_backward(Tensor grad_hy,
Tensor workspace,
boolean has_bias) |
static TensorTensorTuple |
torch._thnn_fused_gru_cell(Tensor input_gates,
Tensor hidden_gates,
Tensor hx) |
static TensorTensorTuple |
torch._thnn_fused_gru_cell(Tensor input_gates,
Tensor hidden_gates,
Tensor hx,
TensorOptional input_bias,
TensorOptional hidden_bias) |
static TensorTensorTensorTensorTensorTuple |
torch._thnn_fused_lstm_cell_backward(TensorOptional grad_hy,
TensorOptional grad_cy,
Tensor cx,
Tensor cy,
Tensor workspace,
boolean has_bias) |
static TensorTensorTensorTuple |
torch._thnn_fused_lstm_cell(Tensor input_gates,
Tensor hidden_gates,
Tensor cx) |
static TensorTensorTensorTuple |
torch._thnn_fused_lstm_cell(Tensor input_gates,
Tensor hidden_gates,
Tensor cx,
TensorOptional input_bias,
TensorOptional hidden_bias) |
static Tensor |
torch._to_copy(Tensor self) |
static Tensor |
torch._to_copy(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
boolean non_blocking,
MemoryFormatOptional memory_format) |
static Tensor |
torch._to_copy(Tensor self,
TensorOptions options,
boolean non_blocking,
MemoryFormatOptional memory_format) |
static Tensor |
torch._trilinear(Tensor i1,
Tensor i2,
Tensor i3,
long[] expand1,
long[] expand2,
long[] expand3,
long... sumdim) |
static Tensor |
torch._trilinear(Tensor i1,
Tensor i2,
Tensor i3,
long[] expand1,
long[] expand2,
long[] expand3,
long[] sumdim,
long unroll_dim) |
static Tensor |
torch._trilinear(Tensor i1,
Tensor i2,
Tensor i3,
LongArrayRef expand1,
LongArrayRef expand2,
LongArrayRef expand3,
LongArrayRef sumdim) |
static Tensor |
torch._trilinear(Tensor i1,
Tensor i2,
Tensor i3,
LongArrayRef expand1,
LongArrayRef expand2,
LongArrayRef expand3,
LongArrayRef sumdim,
long unroll_dim) |
static TensorTensorTuple |
torch._unique(Tensor self) |
static TensorTensorTuple |
torch._unique(Tensor self,
boolean sorted,
boolean return_inverse) |
static TensorTensorTensorTuple |
torch._unique2(Tensor self) |
static TensorTensorTensorTuple |
torch._unique2(Tensor self,
boolean sorted,
boolean return_inverse,
boolean return_counts) |
static TensorTensorTuple |
torch._unpack_dual(Tensor dual,
long level) |
static LongVector |
torch._unpool_output_size(Tensor input,
long[] kernel_size,
long[] stride,
long[] padding,
LongVectorOptional output_size) |
static LongVector |
torch._unpool_output_size(Tensor input,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongVectorOptional output_size) |
static Tensor |
torch._unsafe_view(Tensor self,
long... size) |
static Tensor |
torch._unsafe_view(Tensor self,
LongArrayRef size) |
static boolean |
torch._use_cudnn_ctc_loss(Tensor log_probs,
Tensor targets,
long[] input_lengths,
long[] target_lengths,
long blank) |
static boolean |
torch._use_cudnn_ctc_loss(Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths,
long blank) |
static void |
torch._validate_sparse_coo_tensor_args(Tensor indices,
Tensor values,
long... size) |
static void |
torch._validate_sparse_coo_tensor_args(Tensor indices,
Tensor values,
LongArrayRef size) |
static void |
torch._validate_sparse_csr_tensor_args(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long... size) |
static void |
torch._validate_sparse_csr_tensor_args(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size) |
static TensorTensorTuple |
torch._weight_norm_cuda_interface_backward(Tensor grad_w,
Tensor saved_v,
Tensor saved_g,
Tensor saved_norms,
long dim) |
static TensorTensorTuple |
torch._weight_norm_cuda_interface(Tensor v,
Tensor g) |
static TensorTensorTuple |
torch._weight_norm_cuda_interface(Tensor v,
Tensor g,
long dim) |
static TensorTensorTuple |
torch._weight_norm_differentiable_backward(Tensor grad_w,
Tensor saved_v,
Tensor saved_g,
Tensor saved_norms,
long dim) |
static Tensor |
torch._weight_norm(Tensor v,
Tensor g) |
static Tensor |
torch._weight_norm(Tensor v,
Tensor g,
long dim) |
static Tensor |
torch.abs_(Tensor self) |
static Tensor |
torch.abs_out(Tensor out,
Tensor self) |
static Tensor |
torch.abs_outf(Tensor self,
Tensor out) |
static Tensor |
torch.abs(Tensor self) |
static Tensor |
torch.absolute_out(Tensor out,
Tensor self) |
static Tensor |
torch.absolute_outf(Tensor self,
Tensor out) |
static Tensor |
torch.absolute(Tensor self) |
static Tensor |
torch.acos_(Tensor self) |
static Tensor |
torch.acos_out(Tensor out,
Tensor self) |
static Tensor |
torch.acos_outf(Tensor self,
Tensor out) |
static Tensor |
torch.acos(Tensor self) |
static Tensor |
torch.acosh_(Tensor self) |
static Tensor |
torch.acosh_out(Tensor out,
Tensor self) |
static Tensor |
torch.acosh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.acosh(Tensor self) |
static Tensor |
torch.adaptive_avg_pool1d(Tensor input,
AdaptiveAvgPool1dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_avg_pool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.adaptive_avg_pool1d(Tensor self,
long... output_size) |
static Tensor |
torch.adaptive_avg_pool1d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_avg_pool1d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_avg_pool2d_out(Tensor out,
Tensor self,
long... output_size) |
static Tensor |
torch.adaptive_avg_pool2d_out(Tensor out,
Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_avg_pool2d_outf(Tensor self,
long[] output_size,
Tensor out) |
static Tensor |
torch.adaptive_avg_pool2d_outf(Tensor self,
LongArrayRef output_size,
Tensor out) |
static Tensor |
torch.adaptive_avg_pool2d(Tensor input,
AdaptiveAvgPool2dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_avg_pool2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.adaptive_avg_pool2d(Tensor self,
long... output_size) |
static Tensor |
torch.adaptive_avg_pool2d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_avg_pool2d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_avg_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self) |
static Tensor |
torch.adaptive_avg_pool3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor grad_input) |
static Tensor |
torch.adaptive_avg_pool3d_out(Tensor out,
Tensor self,
long... output_size) |
static Tensor |
torch.adaptive_avg_pool3d_out(Tensor out,
Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_avg_pool3d_outf(Tensor self,
long[] output_size,
Tensor out) |
static Tensor |
torch.adaptive_avg_pool3d_outf(Tensor self,
LongArrayRef output_size,
Tensor out) |
static Tensor |
torch.adaptive_avg_pool3d(Tensor input,
AdaptiveAvgPool3dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_avg_pool3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.adaptive_avg_pool3d(Tensor self,
long... output_size) |
static Tensor |
torch.adaptive_avg_pool3d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_avg_pool3d(Tensor input,
LongPointer output_size) |
static TensorTensorTuple |
torch.adaptive_max_pool1d_with_indices(Tensor input,
AdaptiveMaxPool1dOptions options)
See the documentation for
torch::nn::functional::AdaptiveMaxPool1dFuncOptions class to learn what
optional arguments are supported for this functional. |
static TensorTensorTuple |
torch.adaptive_max_pool1d_with_indices(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_max_pool1d(Tensor input,
AdaptiveMaxPool1dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_max_pool1d
/** about the exact behavior of this functional.
|
static TensorTensorTuple |
torch.adaptive_max_pool1d(Tensor self,
long... output_size) |
static TensorTensorTuple |
torch.adaptive_max_pool1d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_max_pool1d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_max_pool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices) |
static Tensor |
torch.adaptive_max_pool2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.adaptive_max_pool2d_backward(Tensor grad_output,
Tensor self,
Tensor indices) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool2d_out(Tensor out,
Tensor indices,
Tensor self,
long... output_size) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool2d_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef output_size) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool2d_outf(Tensor self,
long[] output_size,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool2d_outf(Tensor self,
LongArrayRef output_size,
Tensor out,
Tensor indices) |
static TensorTensorTuple |
torch.adaptive_max_pool2d_with_indices(Tensor input,
AdaptiveMaxPool2dOptions options)
See the documentation for
torch::nn::functional::AdaptiveMaxPool2dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static TensorTensorTuple |
torch.adaptive_max_pool2d_with_indices(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_max_pool2d(Tensor input,
AdaptiveMaxPool2dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_max_pool2d
/** about the exact behavior of this functional.
|
static TensorTensorTuple |
torch.adaptive_max_pool2d(Tensor self,
long... output_size) |
static TensorTensorTuple |
torch.adaptive_max_pool2d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_max_pool2d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_max_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices) |
static Tensor |
torch.adaptive_max_pool3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.adaptive_max_pool3d_backward(Tensor grad_output,
Tensor self,
Tensor indices) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool3d_out(Tensor out,
Tensor indices,
Tensor self,
long... output_size) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool3d_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef output_size) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool3d_outf(Tensor self,
long[] output_size,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.adaptive_max_pool3d_outf(Tensor self,
LongArrayRef output_size,
Tensor out,
Tensor indices) |
static TensorTensorTuple |
torch.adaptive_max_pool3d_with_indices(Tensor input,
AdaptiveMaxPool3dOptions options)
See the documentation for
torch::nn::functional::AdaptiveMaxPool3dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static TensorTensorTuple |
torch.adaptive_max_pool3d_with_indices(Tensor input,
LongPointer output_size) |
static Tensor |
torch.adaptive_max_pool3d(Tensor input,
AdaptiveMaxPool3dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.adaptive_max_pool3d
/** about the exact behavior of this functional.
|
static TensorTensorTuple |
torch.adaptive_max_pool3d(Tensor self,
long... output_size) |
static TensorTensorTuple |
torch.adaptive_max_pool3d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.adaptive_max_pool3d(Tensor input,
LongPointer output_size) |
static Tensor |
torch.add_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.add_out(Tensor out,
Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.add_outf(Tensor self,
Tensor other,
Scalar alpha,
Tensor out) |
static Tensor |
torch.add(Scalar x,
Tensor y) |
static Tensor |
torch.add(Tensor self,
Scalar other) |
static Tensor |
torch.add(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch.add(Tensor self,
Tensor other) |
static Tensor |
torch.add(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.addbmm_out(Tensor out,
Tensor self,
Tensor batch1,
Tensor batch2) |
static Tensor |
torch.addbmm_out(Tensor out,
Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addbmm_outf(Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.addbmm(Tensor self,
Tensor batch1,
Tensor batch2) |
static Tensor |
torch.addbmm(Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addcdiv_out(Tensor out,
Tensor self,
Tensor tensor1,
Tensor tensor2) |
static Tensor |
torch.addcdiv_out(Tensor out,
Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value) |
static Tensor |
torch.addcdiv_outf(Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value,
Tensor out) |
static Tensor |
torch.addcdiv(Tensor self,
Tensor tensor1,
Tensor tensor2) |
static Tensor |
torch.addcdiv(Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value) |
static Tensor |
torch.addcmul_out(Tensor out,
Tensor self,
Tensor tensor1,
Tensor tensor2) |
static Tensor |
torch.addcmul_out(Tensor out,
Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value) |
static Tensor |
torch.addcmul_outf(Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value,
Tensor out) |
static Tensor |
torch.addcmul(Tensor self,
Tensor tensor1,
Tensor tensor2) |
static Tensor |
torch.addcmul(Tensor self,
Tensor tensor1,
Tensor tensor2,
Scalar value) |
static void |
torch.addInputs(JitNode n,
BytePointer name,
Tensor value) |
static void |
torch.addInputs(JitNode n,
String name,
Tensor value) |
static Tensor |
torch.addmm_out(Tensor out,
Tensor self,
Tensor mat1,
Tensor mat2) |
static Tensor |
torch.addmm_out(Tensor out,
Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addmm_outf(Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.addmm(Tensor self,
Tensor mat1,
Tensor mat2) |
static Tensor |
torch.addmm(Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addmv_(Tensor self,
Tensor mat,
Tensor vec) |
static Tensor |
torch.addmv_(Tensor self,
Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addmv_out(Tensor out,
Tensor self,
Tensor mat,
Tensor vec) |
static Tensor |
torch.addmv_out(Tensor out,
Tensor self,
Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addmv_outf(Tensor self,
Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.addmv(Tensor self,
Tensor mat,
Tensor vec) |
static Tensor |
torch.addmv(Tensor self,
Tensor mat,
Tensor vec,
Scalar beta,
Scalar alpha) |
static void |
torch.addOutput(JitNode node,
Tensor tensor) |
static Tensor |
torch.addr_out(Tensor out,
Tensor self,
Tensor vec1,
Tensor vec2) |
static Tensor |
torch.addr_out(Tensor out,
Tensor self,
Tensor vec1,
Tensor vec2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.addr_outf(Tensor self,
Tensor vec1,
Tensor vec2,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.addr(Tensor self,
Tensor vec1,
Tensor vec2) |
static Tensor |
torch.addr(Tensor self,
Tensor vec1,
Tensor vec2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.affine_grid_generator_backward(Tensor grad,
long[] size,
boolean align_corners) |
static Tensor |
torch.affine_grid_generator_backward(Tensor grad,
LongArrayRef size,
boolean align_corners) |
static Tensor |
torch.affine_grid_generator(Tensor theta,
long[] size,
boolean align_corners) |
static Tensor |
torch.affine_grid_generator(Tensor theta,
LongArrayRef size,
boolean align_corners) |
static Tensor |
torch.affine_grid(Tensor theta,
long... size) |
static Tensor |
torch.affine_grid(Tensor theta,
long[] size,
boolean align_corners) |
static Tensor |
torch.affine_grid(Tensor theta,
LongArrayRef size) |
static Tensor |
torch.affine_grid(Tensor theta,
LongArrayRef size,
boolean align_corners) |
static Tensor |
torch.alias(Tensor self) |
static Tensor |
torch.all_out(Tensor out,
Tensor self) |
static Tensor |
torch.all_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.all_out(Tensor out,
Tensor self,
Dimname dim,
boolean keepdim) |
static Tensor |
torch.all_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.all_out(Tensor out,
Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.all_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.all_outf(Tensor self,
long dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.all_outf(Tensor self,
Tensor out) |
static Tensor |
torch.all(Tensor self) |
static Tensor |
torch.all(Tensor self,
Dimname dim) |
static Tensor |
torch.all(Tensor self,
Dimname dim,
boolean keepdim) |
static Tensor |
torch.all(Tensor self,
long dim) |
static Tensor |
torch.all(Tensor self,
long dim,
boolean keepdim) |
static boolean |
torch.allclose(Tensor self,
Tensor other) |
static boolean |
torch.allclose(Tensor self,
Tensor other,
double rtol,
double atol,
boolean equal_nan) |
static Tensor |
torch.alpha_dropout_(Tensor self,
double p,
boolean train) |
static Tensor |
torch.alpha_dropout(Tensor input) |
static Tensor |
torch.alpha_dropout(Tensor input,
AlphaDropoutFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.alpha_dropout
/** about the exact behavior of this functional.
|
static Tensor |
torch.alpha_dropout(Tensor input,
double p,
boolean train) |
static Tensor |
torch.alpha_dropout(Tensor input,
double p,
boolean training,
boolean inplace) |
static Tensor |
torch.amax_out(Tensor out,
Tensor self) |
static Tensor |
torch.amax_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.amax_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.amax_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.amax_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.amax(Tensor self) |
static Tensor |
torch.amax(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.amax(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.amin_out(Tensor out,
Tensor self) |
static Tensor |
torch.amin_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.amin_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.amin_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.amin_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.amin(Tensor self) |
static Tensor |
torch.amin(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.amin(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.aminmax_out(Tensor min,
Tensor max,
Tensor self) |
static PointerPointer<Tensor> |
torch.aminmax_out(Tensor min,
Tensor max,
Tensor self,
LongOptional dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.aminmax_outf(Tensor self,
LongOptional dim,
boolean keepdim,
Tensor min,
Tensor max) |
static TensorTensorTuple |
torch.aminmax(Tensor self) |
static TensorTensorTuple |
torch.aminmax(Tensor self,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.and(Scalar x,
Tensor y) |
static Tensor |
torch.and(Tensor x,
Scalar y) |
static Tensor |
torch.and(Tensor x,
Tensor y) |
static Tensor |
torch.angle_out(Tensor out,
Tensor self) |
static Tensor |
torch.angle_outf(Tensor self,
Tensor out) |
static Tensor |
torch.angle(Tensor self) |
static Tensor |
torch.any_out(Tensor out,
Tensor self) |
static Tensor |
torch.any_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.any_out(Tensor out,
Tensor self,
Dimname dim,
boolean keepdim) |
static Tensor |
torch.any_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.any_out(Tensor out,
Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.any_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.any_outf(Tensor self,
long dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.any_outf(Tensor self,
Tensor out) |
static Tensor |
torch.any(Tensor self) |
static Tensor |
torch.any(Tensor self,
Dimname dim) |
static Tensor |
torch.any(Tensor self,
Dimname dim,
boolean keepdim) |
static Tensor |
torch.any(Tensor self,
long dim) |
static Tensor |
torch.any(Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.applySelect(Tensor self,
long dim,
long index,
long real_dim,
Device self_device,
long... self_sizes) |
static Tensor |
torch.applySelect(Tensor self,
long dim,
long index,
long real_dim,
Device self_device,
LongArrayRef self_sizes) |
static Tensor |
torch.applySlice(Tensor self,
long dim,
long start,
long stop,
long step,
boolean disable_slice_optimization,
Device self_device,
long... self_sizes) |
static Tensor |
torch.applySlice(Tensor self,
long dim,
long start,
long stop,
long step,
boolean disable_slice_optimization,
Device self_device,
LongArrayRef self_sizes) |
static Tensor |
torch.applySlicing(Tensor self,
TensorIndexArrayRef indices,
TensorVector outIndices,
boolean disable_slice_optimization,
Device self_device,
long... self_sizes) |
static Tensor |
torch.applySlicing(Tensor self,
TensorIndexArrayRef indices,
TensorVector outIndices,
boolean disable_slice_optimization,
Device self_device,
LongArrayRef self_sizes) |
static Tensor |
torch.arange_out(Tensor out,
Scalar end) |
static Tensor |
torch.arange_out(Tensor out,
Scalar start,
Scalar end) |
static Tensor |
torch.arange_out(Tensor out,
Scalar start,
Scalar end,
Scalar step) |
static Tensor |
torch.arange_outf(Scalar start,
Scalar end,
Scalar step,
Tensor out) |
static Tensor |
torch.arange_outf(Scalar end,
Tensor out) |
static Tensor |
torch.arccos_(Tensor self) |
static Tensor |
torch.arccos_out(Tensor out,
Tensor self) |
static Tensor |
torch.arccos_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arccos(Tensor self) |
static Tensor |
torch.arccosh_(Tensor self) |
static Tensor |
torch.arccosh_out(Tensor out,
Tensor self) |
static Tensor |
torch.arccosh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arccosh(Tensor self) |
static Tensor |
torch.arcsin_(Tensor self) |
static Tensor |
torch.arcsin_out(Tensor out,
Tensor self) |
static Tensor |
torch.arcsin_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arcsin(Tensor self) |
static Tensor |
torch.arcsinh_(Tensor self) |
static Tensor |
torch.arcsinh_out(Tensor out,
Tensor self) |
static Tensor |
torch.arcsinh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arcsinh(Tensor self) |
static Tensor |
torch.arctan_(Tensor self) |
static Tensor |
torch.arctan_out(Tensor out,
Tensor self) |
static Tensor |
torch.arctan_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arctan(Tensor self) |
static Tensor |
torch.arctanh_(Tensor self) |
static Tensor |
torch.arctanh_out(Tensor out,
Tensor self) |
static Tensor |
torch.arctanh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.arctanh(Tensor self) |
static Tensor |
torch.argmax_out(Tensor out,
Tensor self) |
static Tensor |
torch.argmax_out(Tensor out,
Tensor self,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.argmax_outf(Tensor self,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.argmax(Tensor self) |
static Tensor |
torch.argmax(Tensor self,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.argmin_out(Tensor out,
Tensor self) |
static Tensor |
torch.argmin_out(Tensor out,
Tensor self,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.argmin_outf(Tensor self,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.argmin(Tensor self) |
static Tensor |
torch.argmin(Tensor self,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.argsort(Tensor self) |
static Tensor |
torch.argsort(Tensor self,
Dimname dim) |
static Tensor |
torch.argsort(Tensor self,
Dimname dim,
boolean descending) |
static Tensor |
torch.argsort(Tensor self,
long dim,
boolean descending) |
static Tensor |
torch.as_strided_(Tensor self,
long[] size,
long... stride) |
static Tensor |
torch.as_strided_(Tensor self,
long[] size,
long[] stride,
LongOptional storage_offset) |
static Tensor |
torch.as_strided_(Tensor self,
LongArrayRef size,
LongArrayRef stride) |
static Tensor |
torch.as_strided_(Tensor self,
LongArrayRef size,
LongArrayRef stride,
LongOptional storage_offset) |
static Tensor |
torch.as_strided(Tensor self,
long[] size,
long... stride) |
static Tensor |
torch.as_strided(Tensor self,
long[] size,
long[] stride,
LongOptional storage_offset) |
static Tensor |
torch.as_strided(Tensor self,
LongArrayRef size,
LongArrayRef stride) |
static Tensor |
torch.as_strided(Tensor self,
LongArrayRef size,
LongArrayRef stride,
LongOptional storage_offset) |
static Tensor |
torch.asin_(Tensor self) |
static Tensor |
torch.asin_out(Tensor out,
Tensor self) |
static Tensor |
torch.asin_outf(Tensor self,
Tensor out) |
static Tensor |
torch.asin(Tensor self) |
static Tensor |
torch.asinh_(Tensor self) |
static Tensor |
torch.asinh_out(Tensor out,
Tensor self) |
static Tensor |
torch.asinh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.asinh(Tensor self) |
static Tensor |
torch.atan_(Tensor self) |
static Tensor |
torch.atan_out(Tensor out,
Tensor self) |
static Tensor |
torch.atan_outf(Tensor self,
Tensor out) |
static Tensor |
torch.atan(Tensor self) |
static Tensor |
torch.atan2_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.atan2_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.atan2(Tensor self,
Tensor other) |
static Tensor |
torch.atanh_(Tensor self) |
static Tensor |
torch.atanh_out(Tensor out,
Tensor self) |
static Tensor |
torch.atanh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.atanh(Tensor self) |
static Tensor |
torch.atleast_1d(Tensor self) |
static Tensor |
torch.atleast_2d(Tensor self) |
static Tensor |
torch.atleast_3d(Tensor self) |
static Tensor |
torch.avg_pool1d(Tensor input,
AvgPool1dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.avg_pool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.avg_pool1d(Tensor self,
long... kernel_size) |
static Tensor |
torch.avg_pool1d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad) |
static Tensor |
torch.avg_pool1d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.avg_pool1d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad) |
static Tensor |
torch.avg_pool1d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
boolean ceil_mode,
boolean count_include_pad) |
static Tensor |
torch.avg_pool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor grad_input) |
static Tensor |
torch.avg_pool2d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor grad_input) |
static Tensor |
torch.avg_pool2d_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_out(Tensor out,
Tensor self,
long... kernel_size) |
static Tensor |
torch.avg_pool2d_out(Tensor out,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_out(Tensor out,
Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.avg_pool2d_out(Tensor out,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d_outf(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor out) |
static Tensor |
torch.avg_pool2d_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor out) |
static Tensor |
torch.avg_pool2d(Tensor input,
AvgPool2dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.avg_pool2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.avg_pool2d(Tensor self,
long... kernel_size) |
static Tensor |
torch.avg_pool2d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.avg_pool2d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool2d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor grad_input) |
static Tensor |
torch.avg_pool3d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor grad_input) |
static Tensor |
torch.avg_pool3d_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_out(Tensor out,
Tensor self,
long... kernel_size) |
static Tensor |
torch.avg_pool3d_out(Tensor out,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_out(Tensor out,
Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.avg_pool3d_out(Tensor out,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d_outf(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor out) |
static Tensor |
torch.avg_pool3d_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override,
Tensor out) |
static Tensor |
torch.avg_pool3d(Tensor input,
AvgPool3dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.avg_pool3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.avg_pool3d(Tensor self,
long... kernel_size) |
static Tensor |
torch.avg_pool3d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.avg_pool3d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.avg_pool3d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
boolean ceil_mode,
boolean count_include_pad,
LongOptional divisor_override) |
static Tensor |
torch.baddbmm_out(Tensor out,
Tensor self,
Tensor batch1,
Tensor batch2) |
static Tensor |
torch.baddbmm_out(Tensor out,
Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.baddbmm_outf(Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.baddbmm(Tensor self,
Tensor batch1,
Tensor batch2) |
static Tensor |
torch.baddbmm(Tensor self,
Tensor batch1,
Tensor batch2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.batch_norm_backward_elemt(Tensor grad_out,
Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional weight,
Tensor mean_dy,
Tensor mean_dy_xmu,
Tensor count) |
static TensorTensorTensorTensorTuple |
torch.batch_norm_backward_reduce(Tensor grad_out,
Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional weight,
boolean input_g,
boolean weight_g,
boolean bias_g) |
static Tensor |
torch.batch_norm_elemt_out(Tensor out,
Tensor input,
TensorOptional weight,
TensorOptional bias,
Tensor mean,
Tensor invstd,
double eps) |
static Tensor |
torch.batch_norm_elemt_outf(Tensor input,
TensorOptional weight,
TensorOptional bias,
Tensor mean,
Tensor invstd,
double eps,
Tensor out) |
static Tensor |
torch.batch_norm_elemt(Tensor input,
TensorOptional weight,
TensorOptional bias,
Tensor mean,
Tensor invstd,
double eps) |
static TensorTensorTuple |
torch.batch_norm_gather_stats_with_counts(Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional running_mean,
TensorOptional running_var,
double momentum,
double eps,
Tensor counts) |
static TensorTensorTuple |
torch.batch_norm_gather_stats(Tensor input,
Tensor mean,
Tensor invstd,
TensorOptional running_mean,
TensorOptional running_var,
double momentum,
double eps,
long count) |
static TensorTensorTuple |
torch.batch_norm_stats(Tensor input,
double eps) |
static TensorTensorTuple |
torch.batch_norm_update_stats(Tensor input,
TensorOptional running_mean,
TensorOptional running_var,
double momentum) |
static Tensor |
torch.batch_norm(Tensor input,
TensorOptional weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean training,
double momentum,
double eps,
boolean cudnn_enabled) |
static Tensor |
torch.batch_norm(Tensor input,
Tensor running_mean,
Tensor running_var) |
static Tensor |
torch.batch_norm(Tensor input,
Tensor running_mean,
Tensor running_var,
BatchNormFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.batch_norm
/** about the exact behavior of this functional.
|
static Tensor |
torch.batch_norm(Tensor input,
Tensor running_mean,
Tensor running_var,
Tensor weight,
Tensor bias,
boolean training,
DoubleOptional momentum,
double eps) |
static Tensor |
torch.bernoulli_out(Tensor out,
Tensor self) |
static Tensor |
torch.bernoulli_out(Tensor out,
Tensor self,
GeneratorOptional generator) |
static Tensor |
torch.bernoulli_outf(Tensor self,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.bernoulli(Tensor self) |
static Tensor |
torch.bernoulli(Tensor self,
double p) |
static Tensor |
torch.bernoulli(Tensor self,
double p,
GeneratorOptional generator) |
static Tensor |
torch.bernoulli(Tensor self,
GeneratorOptional generator) |
static Tensor |
torch.bilinear(Tensor input1,
Tensor input2,
Tensor weight) |
static Tensor |
torch.bilinear(Tensor input1,
Tensor input2,
Tensor weight,
Tensor bias) |
static Tensor |
torch.bilinear(Tensor input1,
Tensor input2,
Tensor weight,
TensorOptional bias) |
static Tensor |
torch.binary_cross_entropy_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction) |
static Tensor |
torch.binary_cross_entropy_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
Tensor grad_input) |
static Tensor |
torch.binary_cross_entropy_backward(Tensor grad_output,
Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy_backward(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction) |
static Tensor |
torch.binary_cross_entropy_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy_out(Tensor out,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction) |
static Tensor |
torch.binary_cross_entropy_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
Tensor out) |
static Tensor |
torch.binary_cross_entropy_with_logits_backward(Tensor grad_output,
Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy_with_logits_backward(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
TensorOptional pos_weight,
long reduction) |
static Tensor |
torch.binary_cross_entropy_with_logits(Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy_with_logits(Tensor input,
Tensor target,
BCEWithLogitsLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.binary_cross_entropy_with_logits
/** about the exact behavior of this functional.
|
static Tensor |
torch.binary_cross_entropy_with_logits(Tensor input,
Tensor target,
Tensor weight,
loss_reduction_t reduction,
Tensor pos_weight) |
static Tensor |
torch.binary_cross_entropy_with_logits(Tensor self,
Tensor target,
TensorOptional weight,
TensorOptional pos_weight,
long reduction) |
static Tensor |
torch.binary_cross_entropy(Tensor self,
Tensor target) |
static Tensor |
torch.binary_cross_entropy(Tensor input,
Tensor target,
BCELossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.binary_cross_entropy
/** about the exact behavior of this functional.
|
static Tensor |
torch.binary_cross_entropy(Tensor input,
Tensor target,
Tensor weight,
loss_reduction_t reduction) |
static Tensor |
torch.binary_cross_entropy(Tensor self,
Tensor target,
TensorOptional weight,
long reduction) |
static Tensor |
torch.bincount(Tensor self) |
static Tensor |
torch.bincount(Tensor self,
TensorOptional weights,
long minlength) |
static Tensor |
torch.binomial(Tensor count,
Tensor prob) |
static Tensor |
torch.binomial(Tensor count,
Tensor prob,
GeneratorOptional generator) |
static Tensor |
torch.bitwise_and_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_and_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_and_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.bitwise_and_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.bitwise_and(Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_and(Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_left_shift_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_left_shift_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_left_shift_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.bitwise_left_shift_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.bitwise_left_shift(Scalar self,
Tensor other) |
static Tensor |
torch.bitwise_left_shift(Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_left_shift(Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_not_out(Tensor out,
Tensor self) |
static Tensor |
torch.bitwise_not_outf(Tensor self,
Tensor out) |
static Tensor |
torch.bitwise_not(Tensor self) |
static Tensor |
torch.bitwise_or_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_or_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_or_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.bitwise_or_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.bitwise_or(Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_or(Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_right_shift_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_right_shift_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_right_shift_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.bitwise_right_shift_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.bitwise_right_shift(Scalar self,
Tensor other) |
static Tensor |
torch.bitwise_right_shift(Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_right_shift(Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_xor_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_xor_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.bitwise_xor_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.bitwise_xor_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.bitwise_xor(Tensor self,
Scalar other) |
static Tensor |
torch.bitwise_xor(Tensor self,
Tensor other) |
static Tensor |
torch.bmm_out(Tensor out,
Tensor self,
Tensor mat2) |
static Tensor |
torch.bmm_outf(Tensor self,
Tensor mat2,
Tensor out) |
static Tensor |
torch.bmm(Tensor self,
Tensor mat2) |
static Tensor |
torch.boolToIndexingTensor(Tensor self,
boolean value,
Device self_device) |
static Tensor |
torch.boolToIndexingTensorCPUOrCUDA(Tensor self,
boolean value) |
static Tensor |
torch.boolToIndexingTensorNonNativeDeviceType(Tensor self,
boolean value) |
static DimnameVector |
torch.broadcast_to_outnames(Tensor tensor,
Tensor reference_tensor,
BytePointer op_name) |
static DimnameVector |
torch.broadcast_to_outnames(Tensor tensor,
Tensor reference_tensor,
String op_name) |
static Tensor |
torch.broadcast_to(Tensor self,
long... size) |
static Tensor |
torch.broadcast_to(Tensor self,
LongArrayRef size) |
static Tensor |
torch.bucketize_out(Tensor out,
Tensor self,
Tensor boundaries) |
static Tensor |
torch.bucketize_out(Tensor out,
Tensor self,
Tensor boundaries,
boolean out_int32,
boolean right) |
static Tensor |
torch.bucketize_outf(Tensor self,
Tensor boundaries,
boolean out_int32,
boolean right,
Tensor out) |
static Tensor |
torch.bucketize(Scalar self,
Tensor boundaries) |
static Tensor |
torch.bucketize(Scalar self,
Tensor boundaries,
boolean out_int32,
boolean right) |
static Tensor |
torch.bucketize(Tensor self,
Tensor boundaries) |
static Tensor |
torch.bucketize(Tensor self,
Tensor boundaries,
boolean out_int32,
boolean right) |
static void |
torch.bump_version(Tensor arg0)
Increments the version count of this
Variable. |
static Tensor |
torch.cat_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.cat_out(Tensor out,
TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.cat_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch.cat_outf(TensorArrayRef tensors,
Dimname dim,
Tensor out) |
static Tensor |
torch.cat_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch.cdist(Tensor x1,
Tensor x2) |
static Tensor |
torch.cdist(Tensor x1,
Tensor x2,
double p,
LongOptional compute_mode) |
static Tensor |
torch.ceil_(Tensor self) |
static Tensor |
torch.ceil_out(Tensor out,
Tensor self) |
static Tensor |
torch.ceil_outf(Tensor self,
Tensor out) |
static Tensor |
torch.ceil(Tensor self) |
static Tensor |
torch.celu_(Tensor self) |
static Tensor |
torch.celu_(Tensor self,
Scalar alpha) |
static Tensor |
torch.celu(Tensor self) |
static Tensor |
torch.celu(Tensor input,
CELUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.celu
/** about the exact behavior of this functional.
|
static Tensor |
torch.celu(Tensor input,
double alpha,
boolean inplace) |
static Tensor |
torch.celu(Tensor self,
Scalar alpha) |
static Tensor |
torch.chain_matmul_out(Tensor out,
TensorArrayRef matrices) |
static Tensor |
torch.chain_matmul_outf(TensorArrayRef matrices,
Tensor out) |
static Tensor |
torch.channel_shuffle(Tensor self,
long groups) |
static void |
torch.check_dim_size(Tensor tensor,
long dim,
long dim_size,
long size) |
static void |
torch.checkDim(BytePointer c,
Tensor tensor,
BytePointer name,
int pos,
long dim) |
static void |
torch.checkDim(String c,
Tensor tensor,
String name,
int pos,
long dim) |
static TensorImpl |
torch.checked_dense_tensor_unwrap(Tensor expr,
BytePointer name,
int pos,
BytePointer api,
boolean allowNull,
torch.DeviceType device_type,
torch.ScalarType scalar_type) |
static TensorImpl |
torch.checked_dense_tensor_unwrap(Tensor expr,
String name,
int pos,
String api,
boolean allowNull,
byte device_type,
torch.ScalarType scalar_type) |
static void |
torch.checkLayout(BytePointer c,
Tensor t,
torch.Layout layout) |
static void |
torch.checkLayout(String c,
Tensor t,
byte layout) |
static Tensor |
torch.cholesky_inverse_out(Tensor out,
Tensor self) |
static Tensor |
torch.cholesky_inverse_out(Tensor out,
Tensor self,
boolean upper) |
static Tensor |
torch.cholesky_inverse_outf(Tensor self,
boolean upper,
Tensor out) |
static Tensor |
torch.cholesky_inverse(Tensor self) |
static Tensor |
torch.cholesky_inverse(Tensor self,
boolean upper) |
static Tensor |
torch.cholesky_out(Tensor out,
Tensor self) |
static Tensor |
torch.cholesky_out(Tensor out,
Tensor self,
boolean upper) |
static Tensor |
torch.cholesky_outf(Tensor self,
boolean upper,
Tensor out) |
static Tensor |
torch.cholesky_solve_out(Tensor out,
Tensor self,
Tensor input2) |
static Tensor |
torch.cholesky_solve_out(Tensor out,
Tensor self,
Tensor input2,
boolean upper) |
static Tensor |
torch.cholesky_solve_outf(Tensor self,
Tensor input2,
boolean upper,
Tensor out) |
static Tensor |
torch.cholesky_solve(Tensor self,
Tensor input2) |
static Tensor |
torch.cholesky_solve(Tensor self,
Tensor input2,
boolean upper) |
static Tensor |
torch.cholesky(Tensor self) |
static Tensor |
torch.cholesky(Tensor self,
boolean upper) |
static TensorTensorTuple |
torch.choose_qparams_optimized(Tensor input,
long numel,
long n_bins,
double ratio,
long bit_width) |
static TensorVector |
torch.chunk(Tensor self,
long chunks) |
static TensorVector |
torch.chunk(Tensor self,
long chunks,
long dim) |
static Tensor |
torch.clamp_(Tensor self) |
static Tensor |
torch.clamp_(Tensor self,
ScalarOptional min) |
static Tensor |
torch.clamp_(Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clamp_(Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clamp_max_(Tensor self,
Scalar max) |
static Tensor |
torch.clamp_max_(Tensor self,
Tensor max) |
static Tensor |
torch.clamp_max_out(Tensor out,
Tensor self,
Scalar max) |
static Tensor |
torch.clamp_max_out(Tensor out,
Tensor self,
Tensor max) |
static Tensor |
torch.clamp_max_outf(Tensor self,
Scalar max,
Tensor out) |
static Tensor |
torch.clamp_max_outf(Tensor self,
Tensor max,
Tensor out) |
static Tensor |
torch.clamp_max(Tensor self,
Scalar max) |
static Tensor |
torch.clamp_max(Tensor self,
Tensor max) |
static Tensor |
torch.clamp_min_(Tensor self,
Scalar min) |
static Tensor |
torch.clamp_min_(Tensor self,
Tensor min) |
static Tensor |
torch.clamp_min_out(Tensor out,
Tensor self,
Scalar min) |
static Tensor |
torch.clamp_min_out(Tensor out,
Tensor self,
Tensor min) |
static Tensor |
torch.clamp_min_outf(Tensor self,
Scalar min,
Tensor out) |
static Tensor |
torch.clamp_min_outf(Tensor self,
Tensor min,
Tensor out) |
static Tensor |
torch.clamp_min(Tensor self,
Scalar min) |
static Tensor |
torch.clamp_min(Tensor self,
Tensor min) |
static Tensor |
torch.clamp_out(Tensor out,
Tensor self) |
static Tensor |
torch.clamp_out(Tensor out,
Tensor self,
ScalarOptional min) |
static Tensor |
torch.clamp_out(Tensor out,
Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clamp_out(Tensor out,
Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clamp_outf(Tensor self,
ScalarOptional min,
ScalarOptional max,
Tensor out) |
static Tensor |
torch.clamp_outf(Tensor self,
TensorOptional min,
TensorOptional max,
Tensor out) |
static Tensor |
torch.clamp(Tensor self) |
static Tensor |
torch.clamp(Tensor self,
ScalarOptional min) |
static Tensor |
torch.clamp(Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clamp(Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clip_(Tensor self) |
static Tensor |
torch.clip_(Tensor self,
ScalarOptional min) |
static Tensor |
torch.clip_(Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clip_(Tensor self,
TensorOptional min,
TensorOptional max) |
static double |
torch.clip_grad_norm_(Tensor parameter,
double max_norm) |
static double |
torch.clip_grad_norm_(Tensor parameter,
double max_norm,
double norm_type,
boolean error_if_nonfinite) |
static void |
torch.clip_grad_value_(Tensor parameter,
double clip_value) |
static Tensor |
torch.clip_out(Tensor out,
Tensor self) |
static Tensor |
torch.clip_out(Tensor out,
Tensor self,
ScalarOptional min) |
static Tensor |
torch.clip_out(Tensor out,
Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clip_out(Tensor out,
Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clip_outf(Tensor self,
ScalarOptional min,
ScalarOptional max,
Tensor out) |
static Tensor |
torch.clip_outf(Tensor self,
TensorOptional min,
TensorOptional max,
Tensor out) |
static Tensor |
torch.clip(Tensor self) |
static Tensor |
torch.clip(Tensor self,
ScalarOptional min) |
static Tensor |
torch.clip(Tensor self,
ScalarOptional min,
ScalarOptional max) |
static Tensor |
torch.clip(Tensor self,
TensorOptional min,
TensorOptional max) |
static Tensor |
torch.clone(Tensor self) |
static Tensor |
torch.clone(Tensor self,
MemoryFormatOptional memory_format) |
static Tensor |
torch.col2im_backward_out(Tensor grad_input,
Tensor grad_output,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.col2im_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.col2im_backward_outf(Tensor grad_output,
long[] kernel_size,
long[] dilation,
long[] padding,
long[] stride,
Tensor grad_input) |
static Tensor |
torch.col2im_backward_outf(Tensor grad_output,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride,
Tensor grad_input) |
static Tensor |
torch.col2im_backward(Tensor grad_output,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.col2im_backward(Tensor grad_output,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.col2im_out(Tensor out,
Tensor self,
long[] output_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.col2im_out(Tensor out,
Tensor self,
LongArrayRef output_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.col2im_outf(Tensor self,
long[] output_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long[] stride,
Tensor out) |
static Tensor |
torch.col2im_outf(Tensor self,
LongArrayRef output_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride,
Tensor out) |
static Tensor |
torch.col2im(Tensor self,
long[] output_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.col2im(Tensor self,
LongArrayRef output_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.column_stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.column_stack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.combinations(Tensor self) |
static Tensor |
torch.combinations(Tensor self,
long r,
boolean with_replacement) |
static Tensor |
torch.complex_out(Tensor out,
Tensor real,
Tensor imag) |
static Tensor |
torch.complex_outf(Tensor real,
Tensor imag,
Tensor out) |
static DimnameVector |
torch.compute_baddbmm_outnames(Tensor result,
Tensor self,
Tensor other,
Tensor bias) |
static DimnameVector |
torch.compute_bmm_outnames(Tensor result,
Tensor self,
Tensor other) |
static DimnameVector |
torch.compute_broadcast_outnames(Tensor self,
Tensor other) |
static DimnameVector |
torch.compute_cdist_outnames(Tensor self,
Tensor other) |
static DimnameVector |
torch.compute_matmul_outnames(Tensor self,
Tensor other) |
static DimnameVector |
torch.compute_squeeze_outnames(Tensor tensor) |
static Tensor |
torch.concat_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.concat_out(Tensor out,
TensorArrayRef tensors,
Dimname dim) |
static Tensor |
torch.concat_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch.concat_outf(TensorArrayRef tensors,
Dimname dim,
Tensor out) |
static Tensor |
torch.concat_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static Tensor |
torch.conj_physical_(Tensor self) |
static Tensor |
torch.conj_physical_out(Tensor out,
Tensor self) |
static Tensor |
torch.conj_physical_outf(Tensor self,
Tensor out) |
static Tensor |
torch.conj_physical(Tensor self) |
static Tensor |
torch.conj(Tensor tensor) |
static Tensor |
torch.constant_(Tensor tensor,
Scalar value)
Fills the given
tensor with the provided value in-place, and returns it. |
static Tensor |
torch.constant_pad_nd(Tensor self,
long... pad) |
static Tensor |
torch.constant_pad_nd(Tensor self,
long[] pad,
Scalar value) |
static Tensor |
torch.constant_pad_nd(Tensor self,
LongArrayRef pad) |
static Tensor |
torch.constant_pad_nd(Tensor self,
LongArrayRef pad,
Scalar value) |
static PointerPointer<Tensor> |
torch.conv_depthwise3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long... dilation) |
static PointerPointer<Tensor> |
torch.conv_depthwise3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static PointerPointer<Tensor> |
torch.conv_depthwise3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.conv_depthwise3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static TensorTensorTensorTuple |
torch.conv_depthwise3d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.conv_depthwise3d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
BoolPointer output_mask) |
static Tensor |
torch.conv_depthwise3d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long... dilation) |
static Tensor |
torch.conv_depthwise3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static TensorTensorTensorTuple |
torch.conv_tbc_backward(Tensor self,
Tensor input,
Tensor weight,
Tensor bias,
long pad) |
static Tensor |
torch.conv_tbc(Tensor self,
Tensor weight,
Tensor bias) |
static Tensor |
torch.conv_tbc(Tensor self,
Tensor weight,
Tensor bias,
long pad) |
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight,
ConvTranspose1dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv_transpose1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight,
Tensor bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight,
Tensor bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose1d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight,
ConvTranspose2dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv_transpose2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight,
Tensor bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight,
Tensor bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose2d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight,
ConvTranspose3dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv_transpose3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight,
Tensor bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight,
Tensor bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long groups,
long... dilation) |
static Tensor |
torch.conv_transpose3d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
long groups,
LongArrayRef dilation) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
Conv1dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
Tensor bias,
LongPointer stride,
conv_padding_t1 padding,
LongPointer dilation,
long groups) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding) |
static Tensor |
torch.conv1d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
Conv2dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
Tensor bias,
LongPointer stride,
conv_padding_t2 padding,
LongPointer dilation,
long groups) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding) |
static Tensor |
torch.conv2d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
Conv3dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.conv3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
Tensor bias,
LongPointer stride,
conv_padding_t3 padding,
LongPointer dilation,
long groups) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
Pointer padding,
long[] dilation,
long groups) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding) |
static Tensor |
torch.conv3d(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
Pointer padding,
LongArrayRef dilation,
long groups) |
static TensorTensorTensorTuple |
torch.convolution_backward_overrideable(Tensor grad_output,
Tensor input,
Tensor weight,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long[] output_padding,
long groups,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.convolution_backward_overrideable(Tensor grad_output,
Tensor input,
Tensor weight,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding,
long groups,
BoolPointer output_mask) |
static Tensor |
torch.convolution_overrideable(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long[] output_padding,
long groups) |
static Tensor |
torch.convolution_overrideable(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding,
long groups) |
static Tensor |
torch.convolution(Tensor input,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
boolean transposed,
long[] output_padding,
long groups) |
static Tensor |
torch.convolution(Tensor input,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean transposed,
LongArrayRef output_padding,
long groups) |
static Tensor |
torch.copy_sparse_to_sparse_(Tensor self,
Tensor src) |
static Tensor |
torch.copy_sparse_to_sparse_(Tensor self,
Tensor src,
boolean non_blocking) |
static void |
torch.copy_to(Tensor dst,
Tensor src) |
static Tensor |
torch.copysign_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.copysign_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.copysign_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.copysign_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.copysign(Tensor self,
Scalar other) |
static Tensor |
torch.copysign(Tensor self,
Tensor other) |
static Tensor |
torch.corrcoef(Tensor self) |
static Tensor |
torch.cos_(Tensor self) |
static Tensor |
torch.cos_out(Tensor out,
Tensor self) |
static Tensor |
torch.cos_outf(Tensor self,
Tensor out) |
static Tensor |
torch.cos(Tensor self) |
static Tensor |
torch.cosh_(Tensor self) |
static Tensor |
torch.cosh_out(Tensor out,
Tensor self) |
static Tensor |
torch.cosh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.cosh(Tensor self) |
static Tensor |
torch.cosine_embedding_loss(Tensor input1,
Tensor input2,
Tensor target) |
static Tensor |
torch.cosine_embedding_loss(Tensor input1,
Tensor input2,
Tensor target,
CosineEmbeddingLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.cosine_embedding_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.cosine_embedding_loss(Tensor input1,
Tensor input2,
Tensor target,
double margin,
long reduction) |
static Tensor |
torch.cosine_embedding_loss(Tensor input1,
Tensor input2,
Tensor target,
double margin,
loss_reduction_t reduction) |
static Tensor |
torch.cosine_similarity(Tensor x1,
Tensor x2) |
static Tensor |
torch.cosine_similarity(Tensor x1,
Tensor x2,
CosineSimilarityOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.cosine_similarity
/** about the exact behavior of this functional.
|
static Tensor |
torch.cosine_similarity(Tensor x1,
Tensor x2,
long dim,
double eps) |
static Tensor |
torch.count_nonzero(Tensor self) |
static Tensor |
torch.count_nonzero(Tensor self,
long... dim) |
static Tensor |
torch.count_nonzero(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.count_nonzero(Tensor self,
LongOptional dim) |
static Tensor |
torch.cov(Tensor self) |
static Tensor |
torch.cov(Tensor self,
long correction,
TensorOptional fweights,
TensorOptional aweights) |
static void |
torch.create_gradient_edge(Tensor variable,
Node function)
Create an
Edge between the given variable and the function, which is
assumed to be the gradient function of this variable (i.e. |
static Tensor |
torch.cross_entropy_loss(Tensor self,
Tensor target) |
static Tensor |
torch.cross_entropy_loss(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
double label_smoothing) |
static Tensor |
torch.cross_entropy(Tensor input,
Tensor target) |
static Tensor |
torch.cross_entropy(Tensor input,
Tensor target,
CrossEntropyLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.cross_entropy
/** about the exact behavior of this functional.
|
static Tensor |
torch.cross_entropy(Tensor input,
Tensor target,
Tensor weight,
long ignore_index,
loss_reduction_t reduction,
double label_smoothing) |
static Tensor |
torch.cross_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.cross_out(Tensor out,
Tensor self,
Tensor other,
LongOptional dim) |
static Tensor |
torch.cross_outf(Tensor self,
Tensor other,
LongOptional dim,
Tensor out) |
static Tensor |
torch.cross(Tensor self,
Tensor other) |
static Tensor |
torch.cross(Tensor self,
Tensor other,
LongOptional dim) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
long[] input_lengths,
long... target_lengths) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
long[] input_lengths,
long[] target_lengths,
long blank,
long reduction,
boolean zero_infinity) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
LongArrayRef input_lengths,
LongArrayRef target_lengths,
long blank,
long reduction,
boolean zero_infinity) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
Tensor input_lengths,
Tensor target_lengths) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
Tensor input_lengths,
Tensor target_lengths,
CTCLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.ctc_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
Tensor input_lengths,
Tensor target_lengths,
long blank,
long reduction,
boolean zero_infinity) |
static Tensor |
torch.ctc_loss(Tensor log_probs,
Tensor targets,
Tensor input_lengths,
Tensor target_lengths,
long blank,
loss_reduction_t reduction,
boolean zero_infinity) |
static Tensor |
torch.cudnn_affine_grid_generator_backward(Tensor grad,
long N,
long C,
long H,
long W) |
static Tensor |
torch.cudnn_affine_grid_generator(Tensor theta,
long N,
long C,
long H,
long W) |
static TensorTensorTensorTuple |
torch.cudnn_batch_norm_backward(Tensor input,
Tensor grad_output,
Tensor weight,
TensorOptional running_mean,
TensorOptional running_var,
TensorOptional save_mean,
TensorOptional save_var,
double epsilon,
Tensor reserveSpace) |
static TensorTensorTensorTensorTuple |
torch.cudnn_batch_norm(Tensor input,
Tensor weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean training,
double exponential_average_factor,
double epsilon) |
static Tensor |
torch.cudnn_convolution_add_relu(Tensor self,
Tensor weight,
Tensor z,
ScalarOptional alpha,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
long groups) |
static Tensor |
torch.cudnn_convolution_add_relu(Tensor self,
Tensor weight,
Tensor z,
ScalarOptional alpha,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.cudnn_convolution_backward_input(long[] self_size,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_backward_input(LongArrayRef self_size,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_backward_weight(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_backward_weight(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static TensorTensorTuple |
torch.cudnn_convolution_backward(Tensor self,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32,
BoolPointer output_mask) |
static TensorTensorTuple |
torch.cudnn_convolution_backward(Tensor self,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32,
BoolPointer output_mask) |
static Tensor |
torch.cudnn_convolution_relu(Tensor self,
Tensor weight,
TensorOptional bias,
long[] stride,
long[] padding,
long[] dilation,
long groups) |
static Tensor |
torch.cudnn_convolution_relu(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.cudnn_convolution_transpose_backward_input(Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose_backward_input(Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose_backward_weight(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose_backward_weight(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static TensorTensorTuple |
torch.cudnn_convolution_transpose_backward(Tensor self,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32,
BoolPointer output_mask) |
static TensorTensorTuple |
torch.cudnn_convolution_transpose_backward(Tensor self,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32,
BoolPointer output_mask) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution_transpose(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
boolean allow_tf32) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.cudnn_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static TensorTensorTuple |
torch.cudnn_grid_sampler_backward(Tensor self,
Tensor grid,
Tensor grad_output) |
static Tensor |
torch.cudnn_grid_sampler(Tensor self,
Tensor grid) |
static boolean |
torch.cudnn_is_acceptable(Tensor self) |
static PointerPointer<Tensor> |
torch.cummax_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.cummax_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.cummax_outf(Tensor self,
Dimname dim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.cummax_outf(Tensor self,
long dim,
Tensor values,
Tensor indices) |
static TensorTensorTuple |
torch.cummax(Tensor self,
Dimname dim) |
static TensorTensorTuple |
torch.cummax(Tensor self,
long dim) |
static Tensor |
torch.cummaxmin_backward(Tensor grad,
Tensor input,
Tensor indices,
long dim) |
static PointerPointer<Tensor> |
torch.cummin_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.cummin_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.cummin_outf(Tensor self,
Dimname dim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.cummin_outf(Tensor self,
long dim,
Tensor values,
Tensor indices) |
static TensorTensorTuple |
torch.cummin(Tensor self,
Dimname dim) |
static TensorTensorTuple |
torch.cummin(Tensor self,
long dim) |
static Tensor |
torch.cumprod_backward(Tensor grad,
Tensor input,
long dim,
Tensor output) |
static Tensor |
torch.cumprod_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.cumprod_out(Tensor out,
Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumprod_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.cumprod_out(Tensor out,
Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumprod_outf(Tensor self,
Dimname dim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.cumprod_outf(Tensor self,
long dim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.cumprod(Tensor self,
Dimname dim) |
static Tensor |
torch.cumprod(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumprod(Tensor self,
long dim) |
static Tensor |
torch.cumprod(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumsum_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.cumsum_out(Tensor out,
Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumsum_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.cumsum_out(Tensor out,
Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumsum_outf(Tensor self,
Dimname dim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.cumsum_outf(Tensor self,
long dim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.cumsum(Tensor self,
Dimname dim) |
static Tensor |
torch.cumsum(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumsum(Tensor self,
long dim) |
static Tensor |
torch.cumsum(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.cumulative_trapezoid(Tensor y) |
static Tensor |
torch.cumulative_trapezoid(Tensor y,
Scalar dx,
long dim) |
static Tensor |
torch.cumulative_trapezoid(Tensor y,
Tensor x) |
static Tensor |
torch.cumulative_trapezoid(Tensor y,
Tensor x,
long dim) |
static Tensor |
torch.deg2rad_(Tensor self) |
static Tensor |
torch.deg2rad_out(Tensor out,
Tensor self) |
static Tensor |
torch.deg2rad_outf(Tensor self,
Tensor out) |
static Tensor |
torch.deg2rad(Tensor self) |
static Tensor |
torch.dequantize(Tensor self) |
static Tensor |
torch.det(Tensor self) |
static Tensor |
torch.detach_(Tensor self) |
static Tensor |
torch.detach(Tensor self) |
static DeviceOptional |
torch.device_of(Tensor t)
Return the Device of a Tensor, if the Tensor is defined.
|
static Tensor |
torch.diag_backward(Tensor grad,
long[] input_sizes,
long diagonal) |
static Tensor |
torch.diag_backward(Tensor grad,
LongArrayRef input_sizes,
long diagonal) |
static Tensor |
torch.diag_embed(Tensor self) |
static Tensor |
torch.diag_embed(Tensor self,
long offset,
long dim1,
long dim2) |
static Tensor |
torch.diag_out(Tensor out,
Tensor self) |
static Tensor |
torch.diag_out(Tensor out,
Tensor self,
long diagonal) |
static Tensor |
torch.diag_outf(Tensor self,
long diagonal,
Tensor out) |
static Tensor |
torch.diag(Tensor self) |
static Tensor |
torch.diag(Tensor self,
long diagonal) |
static Tensor |
torch.diagflat(Tensor self) |
static Tensor |
torch.diagflat(Tensor self,
long offset) |
static Tensor |
torch.diagonal_backward(Tensor grad_output,
long[] input_sizes,
long offset,
long dim1,
long dim2) |
static Tensor |
torch.diagonal_backward(Tensor grad_output,
LongArrayRef input_sizes,
long offset,
long dim1,
long dim2) |
static Tensor |
torch.diagonal(Tensor self) |
static Tensor |
torch.diagonal(Tensor self,
Dimname outdim,
Dimname dim1,
Dimname dim2) |
static Tensor |
torch.diagonal(Tensor self,
Dimname outdim,
Dimname dim1,
Dimname dim2,
long offset) |
static Tensor |
torch.diagonal(Tensor self,
long offset,
long dim1,
long dim2) |
static Tensor |
torch.diff_out(Tensor out,
Tensor self) |
static Tensor |
torch.diff_out(Tensor out,
Tensor self,
long n,
long dim,
TensorOptional prepend,
TensorOptional append) |
static Tensor |
torch.diff_outf(Tensor self,
long n,
long dim,
TensorOptional prepend,
TensorOptional append,
Tensor out) |
static Tensor |
torch.diff(Tensor self) |
static Tensor |
torch.diff(Tensor self,
long n,
long dim,
TensorOptional prepend,
TensorOptional append) |
static Tensor |
torch.digamma_out(Tensor out,
Tensor self) |
static Tensor |
torch.digamma_outf(Tensor self,
Tensor out) |
static Tensor |
torch.digamma(Tensor self) |
static long |
torch.dimname_to_position(Tensor tensor,
Dimname dim) |
static LongVector |
torch.dimnames_to_positions(Tensor tensor,
DimnameArrayRef dims) |
static Tensor |
torch.dirac_(Tensor tensor)
Fills the given
tensor with the Dirac delta function in-place, and returns
it. |
static Tensor |
torch.dispatch_index_put_(Tensor self,
TensorVector indices,
Tensor value) |
static Tensor |
torch.dispatch_index(Tensor self,
TensorVector indices) |
static Tensor |
torch.dist(Tensor self,
Tensor other) |
static Tensor |
torch.dist(Tensor self,
Tensor other,
Scalar p) |
static Tensor |
torch.div_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.div_out(Tensor out,
Tensor self,
Tensor other,
Pointer rounding_mode) |
static Tensor |
torch.div_outf(Tensor self,
Tensor other,
Pointer rounding_mode,
Tensor out) |
static Tensor |
torch.div_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.div(Tensor self,
Scalar other) |
static Tensor |
torch.div(Tensor self,
Scalar other,
Pointer rounding_mode) |
static Tensor |
torch.div(Tensor self,
Tensor other) |
static Tensor |
torch.div(Tensor self,
Tensor other,
Pointer rounding_mode) |
static Tensor |
torch.divide_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.divide_out(Tensor out,
Tensor self,
Tensor other,
Pointer rounding_mode) |
static Tensor |
torch.divide_outf(Tensor self,
Tensor other,
Pointer rounding_mode,
Tensor out) |
static Tensor |
torch.divide_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.divide(Scalar x,
Tensor y) |
static Tensor |
torch.divide(Tensor self,
Scalar other) |
static Tensor |
torch.divide(Tensor self,
Scalar other,
Pointer rounding_mode) |
static Tensor |
torch.divide(Tensor self,
Tensor other) |
static Tensor |
torch.divide(Tensor self,
Tensor other,
Pointer rounding_mode) |
static Tensor |
torch.dot_out(Tensor out,
Tensor self,
Tensor tensor) |
static Tensor |
torch.dot_outf(Tensor self,
Tensor tensor,
Tensor out) |
static Tensor |
torch.dot(Tensor self,
Tensor tensor) |
static Tensor |
torch.dropout_(Tensor self,
double p,
boolean train) |
static Tensor |
torch.dropout(Tensor input) |
static Tensor |
torch.dropout(Tensor input,
double p,
boolean train) |
static Tensor |
torch.dropout(Tensor input,
double p,
boolean training,
boolean inplace)
Computes the p-norm distance between every pair of row vectors in the input.
|
static Tensor |
torch.dropout(Tensor input,
DropoutFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.dropout
/** about the exact behavior of this functional.
|
static Tensor |
torch.dropout2d(Tensor input) |
static Tensor |
torch.dropout2d(Tensor input,
double p,
boolean training,
boolean inplace) |
static Tensor |
torch.dropout2d(Tensor input,
DropoutFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.dropout2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.dropout3d(Tensor input) |
static Tensor |
torch.dropout3d(Tensor input,
double p,
boolean training,
boolean inplace) |
static Tensor |
torch.dropout3d(Tensor input,
DropoutFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.dropout3d
/** about the exact behavior of this functional.
|
static TensorVector |
torch.dsplit(Tensor self,
long... indices) |
static TensorVector |
torch.dsplit(Tensor self,
long sections) |
static TensorVector |
torch.dsplit(Tensor self,
LongArrayRef indices) |
static Tensor |
torch.dstack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.dstack_outf(TensorArrayRef tensors,
Tensor out) |
static PointerPointer<Tensor> |
torch.eig_out(Tensor e,
Tensor v,
Tensor self) |
static PointerPointer<Tensor> |
torch.eig_out(Tensor e,
Tensor v,
Tensor self,
boolean eigenvectors) |
static PointerPointer<Tensor> |
torch.eig_outf(Tensor self,
boolean eigenvectors,
Tensor e,
Tensor v) |
static TensorTensorTuple |
torch.eig(Tensor self) |
static TensorTensorTuple |
torch.eig(Tensor self,
boolean eigenvectors) |
static Tensor |
torch.elu_(Tensor self) |
static Tensor |
torch.elu_(Tensor self,
Scalar alpha,
Scalar scale,
Scalar input_scale) |
static Tensor |
torch.elu_backward_out(Tensor grad_input,
Tensor grad_output,
Scalar alpha,
Scalar scale,
Scalar input_scale,
boolean is_result,
Tensor self_or_result) |
static Tensor |
torch.elu_backward_outf(Tensor grad_output,
Scalar alpha,
Scalar scale,
Scalar input_scale,
boolean is_result,
Tensor self_or_result,
Tensor grad_input) |
static Tensor |
torch.elu_backward(Tensor grad_output,
Scalar alpha,
Scalar scale,
Scalar input_scale,
boolean is_result,
Tensor self_or_result) |
static Tensor |
torch.elu_out(Tensor out,
Tensor self) |
static Tensor |
torch.elu_out(Tensor out,
Tensor self,
Scalar alpha,
Scalar scale,
Scalar input_scale) |
static Tensor |
torch.elu_outf(Tensor self,
Scalar alpha,
Scalar scale,
Scalar input_scale,
Tensor out) |
static Tensor |
torch.elu(Tensor self) |
static Tensor |
torch.elu(Tensor input,
double alpha,
boolean inplace)
Options for the
InstanceNorm3d module. |
static Tensor |
torch.elu(Tensor input,
ELUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.elu
/** about the exact behavior of this functional.
|
static Tensor |
torch.elu(Tensor self,
Scalar alpha,
Scalar scale,
Scalar input_scale) |
static Tensor |
torch.embedding_backward(Tensor grad,
Tensor indices,
long num_weights,
long padding_idx,
boolean scale_grad_by_freq,
boolean sparse) |
static Tensor |
torch.embedding_bag(Tensor input,
Tensor weight) |
static Tensor |
torch.embedding_bag(Tensor input,
Tensor weight,
EmbeddingBagFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.embedding_bag
/** about the exact behavior of this functional.
|
static TensorTensorTensorTensorTuple |
torch.embedding_bag(Tensor weight,
Tensor indices,
Tensor offsets) |
static TensorTensorTensorTensorTuple |
torch.embedding_bag(Tensor weight,
Tensor indices,
Tensor offsets,
boolean scale_grad_by_freq,
long mode,
boolean sparse,
TensorOptional per_sample_weights,
boolean include_last_offset) |
static TensorTensorTensorTensorTuple |
torch.embedding_bag(Tensor weight,
Tensor indices,
Tensor offsets,
boolean scale_grad_by_freq,
long mode,
boolean sparse,
TensorOptional per_sample_weights,
boolean include_last_offset,
LongOptional padding_idx) |
static Tensor |
torch.embedding_bag(Tensor input,
Tensor weight,
Tensor offsets,
DoubleOptional max_norm,
double norm_type,
boolean scale_grad_by_freq,
EmbeddingBagMode mode,
boolean sparse,
Tensor per_sample_weights,
boolean include_last_offset,
LongOptional padding_idx) |
static Tensor |
torch.embedding_dense_backward(Tensor grad_output,
Tensor indices,
long num_weights,
long padding_idx,
boolean scale_grad_by_freq) |
static Tensor |
torch.embedding_renorm_(Tensor self,
Tensor indices,
double max_norm,
double norm_type) |
static Tensor |
torch.embedding_sparse_backward(Tensor grad,
Tensor indices,
long num_weights,
long padding_idx,
boolean scale_grad_by_freq) |
static Tensor |
torch.embedding(Tensor weight,
Tensor indices) |
static Tensor |
torch.embedding(Tensor input,
Tensor weight,
EmbeddingFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.embedding
/** about the exact behavior of this functional.
|
static Tensor |
torch.embedding(Tensor weight,
Tensor indices,
long padding_idx,
boolean scale_grad_by_freq,
boolean sparse) |
static Tensor |
torch.embedding(Tensor input,
Tensor weight,
LongOptional padding_idx,
DoubleOptional max_norm,
double norm_type,
boolean scale_grad_by_freq,
boolean sparse) |
static Tensor |
torch.empty_like(Tensor self) |
static Tensor |
torch.empty_like(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_like(Tensor self,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_out(Tensor out,
long... size) |
static Tensor |
torch.empty_out(Tensor out,
long[] size,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_out(Tensor out,
LongArrayRef size) |
static Tensor |
torch.empty_out(Tensor out,
LongArrayRef size,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_outf(long[] size,
MemoryFormatOptional memory_format,
Tensor out) |
static Tensor |
torch.empty_outf(LongArrayRef size,
MemoryFormatOptional memory_format,
Tensor out) |
static Tensor |
torch.empty_quantized(long[] size,
Tensor qtensor) |
static Tensor |
torch.empty_quantized(long[] size,
Tensor qtensor,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_quantized(long[] size,
Tensor qtensor,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_quantized(LongArrayRef size,
Tensor qtensor) |
static Tensor |
torch.empty_quantized(LongArrayRef size,
Tensor qtensor,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.empty_quantized(LongArrayRef size,
Tensor qtensor,
TensorOptions options,
MemoryFormatOptional memory_format) |
static void |
torch.ensureUniqueIfOutOfPlaced(BytePointer name,
Tensor tensor) |
static void |
torch.ensureUniqueIfOutOfPlaced(String name,
Tensor tensor) |
static Tensor |
torch.eq_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.eq_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.eq_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.eq_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.eq(Tensor self,
Scalar other) |
static Tensor |
torch.eq(Tensor self,
Tensor other) |
static boolean |
torch.equal_if_defined(Tensor t1,
Tensor t2)
Sets the global random seed for all newly created CPU and CUDA tensors.
|
static boolean |
torch.equal(Tensor self,
Tensor other) |
static Tensor |
torch.equals(Scalar x,
Tensor y) |
static Tensor |
torch.equals(Tensor x,
Scalar y) |
static Tensor |
torch.equals(Tensor x,
Tensor y) |
static Tensor |
torch.erf_(Tensor self) |
static Tensor |
torch.erf_out(Tensor out,
Tensor self) |
static Tensor |
torch.erf_outf(Tensor self,
Tensor out) |
static Tensor |
torch.erf(Tensor self) |
static Tensor |
torch.erfc_(Tensor self) |
static Tensor |
torch.erfc_out(Tensor out,
Tensor self) |
static Tensor |
torch.erfc_outf(Tensor self,
Tensor out) |
static Tensor |
torch.erfc(Tensor self) |
static Tensor |
torch.erfinv_out(Tensor out,
Tensor self) |
static Tensor |
torch.erfinv_outf(Tensor self,
Tensor out) |
static Tensor |
torch.erfinv(Tensor self) |
static Tensor |
torch.exp_(Tensor self) |
static Tensor |
torch.exp_out(Tensor out,
Tensor self) |
static Tensor |
torch.exp_outf(Tensor self,
Tensor out) |
static Tensor |
torch.exp(Tensor self) |
static Tensor |
torch.exp2_(Tensor self) |
static Tensor |
torch.exp2_out(Tensor out,
Tensor self) |
static Tensor |
torch.exp2_outf(Tensor self,
Tensor out) |
static Tensor |
torch.exp2(Tensor self) |
static TensorMaybeOwned |
torch.expand_inplace(Tensor tensor,
Tensor to_expand) |
static TensorMaybeOwned |
torch.expand_inplace(Tensor tensor,
Tensor to_expand,
BytePointer api_name) |
static TensorMaybeOwned |
torch.expand_inplace(Tensor tensor,
Tensor to_expand,
String api_name) |
static TensorMaybeOwnedTensorMaybeOwnedTuple |
torch.expand_inplace(Tensor tensor,
Tensor to_expand1,
Tensor to_expand2) |
static TensorMaybeOwnedTensorMaybeOwnedTuple |
torch.expand_inplace(Tensor tensor,
Tensor to_expand1,
Tensor to_expand2,
BytePointer api_name) |
static TensorMaybeOwnedTensorMaybeOwnedTuple |
torch.expand_inplace(Tensor tensor,
Tensor to_expand1,
Tensor to_expand2,
String api_name) |
static TensorMaybeOwnedTensorMaybeOwnedTuple |
torch.expand_outplace(Tensor to_expand1,
Tensor to_expand2) |
static TensorMaybeOwnedTensorMaybeOwnedTuple |
torch.expand_outplace(Tensor to_expand1,
Tensor to_expand2,
BytePointer api_name) |
static TensorMaybeOwnedTensorMaybeOwnedTuple |
torch.expand_outplace(Tensor to_expand1,
Tensor to_expand2,
String api_name) |
static TensorMaybeOwnedTensorMaybeOwnedTensorMaybeOwnedTuple |
torch.expand_outplace(Tensor to_expand1,
Tensor to_expand2,
Tensor to_expand3) |
static TensorMaybeOwnedTensorMaybeOwnedTensorMaybeOwnedTuple |
torch.expand_outplace(Tensor to_expand1,
Tensor to_expand2,
Tensor to_expand3,
BytePointer api_name) |
static TensorMaybeOwnedTensorMaybeOwnedTensorMaybeOwnedTuple |
torch.expand_outplace(Tensor to_expand1,
Tensor to_expand2,
Tensor to_expand3,
String api_name) |
static TensorMaybeOwned |
torch.expand_size(Tensor to_expand,
long... sizes) |
static TensorMaybeOwned |
torch.expand_size(Tensor to_expand,
long[] sizes,
String api_name) |
static TensorMaybeOwned |
torch.expand_size(Tensor to_expand,
LongArrayRef sizes) |
static TensorMaybeOwned |
torch.expand_size(Tensor to_expand,
LongArrayRef sizes,
BytePointer api_name) |
static Tensor |
torch.expm1_(Tensor self) |
static Tensor |
torch.expm1_out(Tensor out,
Tensor self) |
static Tensor |
torch.expm1_outf(Tensor self,
Tensor out) |
static Tensor |
torch.expm1(Tensor self) |
static Tensor |
torch.eye_(Tensor matrix)
Fills the given 2-dimensional
matrix with an identity matrix. |
static Tensor |
torch.eye_out(Tensor out,
long n) |
static Tensor |
torch.eye_out(Tensor out,
long n,
long m) |
static Tensor |
torch.eye_outf(long n,
long m,
Tensor out) |
static Tensor |
torch.eye_outf(long n,
Tensor out) |
static Tensor |
torch.fake_quantize_per_channel_affine_cachemask_backward(Tensor grad,
Tensor mask) |
static TensorTensorTuple |
torch.fake_quantize_per_channel_affine_cachemask(Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max) |
static Tensor |
torch.fake_quantize_per_channel_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long axis,
long quant_min,
long quant_max) |
static Tensor |
torch.fake_quantize_per_tensor_affine_cachemask_backward(Tensor grad,
Tensor mask) |
static TensorTensorTuple |
torch.fake_quantize_per_tensor_affine_cachemask(Tensor self,
double scale,
long zero_point,
long quant_min,
long quant_max) |
static Tensor |
torch.fake_quantize_per_tensor_affine(Tensor self,
double scale,
long zero_point,
long quant_min,
long quant_max) |
static Tensor |
torch.fake_quantize_per_tensor_affine(Tensor self,
Tensor scale,
Tensor zero_point,
long quant_min,
long quant_max) |
static Tensor |
torch.fbgemm_linear_fp16_weight_fp32_activation(Tensor input,
Tensor packed_weight,
Tensor bias) |
static Tensor |
torch.fbgemm_linear_fp16_weight(Tensor input,
Tensor packed_weight,
Tensor bias) |
static Tensor |
torch.fbgemm_linear_int8_weight_fp32_activation(Tensor input,
Tensor weight,
Tensor packed,
Tensor col_offsets,
Scalar weight_scale,
Scalar weight_zero_point,
Tensor bias) |
static Tensor |
torch.fbgemm_linear_int8_weight(Tensor input,
Tensor weight,
Tensor packed,
Tensor col_offsets,
Scalar weight_scale,
Scalar weight_zero_point,
Tensor bias) |
static TensorTensorDoubleLongTuple |
torch.fbgemm_linear_quantize_weight(Tensor input) |
static Tensor |
torch.fbgemm_pack_gemm_matrix_fp16(Tensor input) |
static Tensor |
torch.fbgemm_pack_quantized_matrix(Tensor input) |
static Tensor |
torch.fbgemm_pack_quantized_matrix(Tensor input,
long K,
long N) |
static Tensor |
torch.feature_alpha_dropout_(Tensor self,
double p,
boolean train) |
static Tensor |
torch.feature_alpha_dropout(Tensor input) |
static Tensor |
torch.feature_alpha_dropout(Tensor input,
double p,
boolean train) |
static Tensor |
torch.feature_alpha_dropout(Tensor input,
double p,
boolean training,
boolean inplace) |
static Tensor |
torch.feature_alpha_dropout(Tensor input,
FeatureAlphaDropoutFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.feature_alpha_dropout
/** about the exact behavior of this functional.
|
static Tensor |
torch.feature_dropout_(Tensor self,
double p,
boolean train) |
static Tensor |
torch.feature_dropout(Tensor input,
double p,
boolean train) |
static Tensor |
torch.fft_fft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_fft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_fft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_fft(Tensor self) |
static Tensor |
torch.fft_fft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_fft2_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_fft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_fft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_fft2_outf(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_fft2_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_fft2(Tensor self) |
static Tensor |
torch.fft_fft2(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_fft2(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_fftfreq_out(Tensor out,
long n) |
static Tensor |
torch.fft_fftfreq_out(Tensor out,
long n,
double d) |
static Tensor |
torch.fft_fftfreq_outf(long n,
double d,
Tensor out) |
static Tensor |
torch.fft_fftn_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_fftn_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_fftn_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_fftn(Tensor self) |
static Tensor |
torch.fft_fftn(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_fftshift(Tensor self) |
static Tensor |
torch.fft_fftshift(Tensor self,
LongArrayRefOptional dim) |
static Tensor |
torch.fft_hfft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_hfft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_hfft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_hfft(Tensor self) |
static Tensor |
torch.fft_hfft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_ifft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_ifft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_ifft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_ifft(Tensor self) |
static Tensor |
torch.fft_ifft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_ifft2_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_ifft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_ifft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_ifft2_outf(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_ifft2_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_ifft2(Tensor self) |
static Tensor |
torch.fft_ifft2(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_ifft2(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_ifftn_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_ifftn_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_ifftn_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_ifftn(Tensor self) |
static Tensor |
torch.fft_ifftn(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_ifftshift(Tensor self) |
static Tensor |
torch.fft_ifftshift(Tensor self,
LongArrayRefOptional dim) |
static Tensor |
torch.fft_ihfft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_ihfft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_ihfft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_ihfft(Tensor self) |
static Tensor |
torch.fft_ihfft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_irfft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_irfft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_irfft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_irfft(Tensor self) |
static Tensor |
torch.fft_irfft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_irfft2_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_irfft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_irfft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_irfft2_outf(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_irfft2_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_irfft2(Tensor self) |
static Tensor |
torch.fft_irfft2(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_irfft2(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_irfftn_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_irfftn_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_irfftn_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_irfftn(Tensor self) |
static Tensor |
torch.fft_irfftn(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_rfft_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_rfft_out(Tensor out,
Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_rfft_outf(Tensor self,
LongOptional n,
long dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_rfft(Tensor self) |
static Tensor |
torch.fft_rfft(Tensor self,
LongOptional n,
long dim,
Pointer norm) |
static Tensor |
torch.fft_rfft2_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_rfft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_rfft2_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_rfft2_outf(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_rfft2_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_rfft2(Tensor self) |
static Tensor |
torch.fft_rfft2(Tensor self,
LongArrayRefOptional s,
long[] dim,
Pointer norm) |
static Tensor |
torch.fft_rfft2(Tensor self,
LongArrayRefOptional s,
LongArrayRef dim,
Pointer norm) |
static Tensor |
torch.fft_rfftfreq_out(Tensor out,
long n) |
static Tensor |
torch.fft_rfftfreq_out(Tensor out,
long n,
double d) |
static Tensor |
torch.fft_rfftfreq_outf(long n,
double d,
Tensor out) |
static Tensor |
torch.fft_rfftn_out(Tensor out,
Tensor self) |
static Tensor |
torch.fft_rfftn_out(Tensor out,
Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fft_rfftn_outf(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm,
Tensor out) |
static Tensor |
torch.fft_rfftn(Tensor self) |
static Tensor |
torch.fft_rfftn(Tensor self,
LongArrayRefOptional s,
LongArrayRefOptional dim,
Pointer norm) |
static Tensor |
torch.fill_(Tensor self,
Scalar value) |
static Tensor |
torch.fill_(Tensor self,
Tensor value) |
static Tensor |
torch.fix_(Tensor self) |
static Tensor |
torch.fix_out(Tensor out,
Tensor self) |
static Tensor |
torch.fix_outf(Tensor self,
Tensor out) |
static Tensor |
torch.fix(Tensor self) |
static Tensor |
torch.flatten(Tensor self) |
static Tensor |
torch.flatten(Tensor self,
DimnameArrayRef dims,
Dimname out_dim) |
static Tensor |
torch.flatten(Tensor self,
Dimname start_dim,
Dimname end_dim,
Dimname out_dim) |
static Tensor |
torch.flatten(Tensor self,
long start_dim,
long end_dim) |
static Tensor |
torch.flatten(Tensor self,
long start_dim,
long end_dim,
Dimname out_dim) |
static Tensor |
torch.flip(Tensor self,
long... dims) |
static Tensor |
torch.flip(Tensor self,
LongArrayRef dims) |
static Tensor |
torch.fliplr(Tensor self) |
static Tensor |
torch.flipud(Tensor self) |
static Tensor |
torch.float_power_out(Tensor out,
Scalar self,
Tensor exponent) |
static Tensor |
torch.float_power_out(Tensor out,
Tensor self,
Scalar exponent) |
static Tensor |
torch.float_power_out(Tensor out,
Tensor self,
Tensor exponent) |
static Tensor |
torch.float_power_outf(Scalar self,
Tensor exponent,
Tensor out) |
static Tensor |
torch.float_power_outf(Tensor self,
Scalar exponent,
Tensor out) |
static Tensor |
torch.float_power_outf(Tensor self,
Tensor exponent,
Tensor out) |
static Tensor |
torch.float_power(Scalar self,
Tensor exponent) |
static Tensor |
torch.float_power(Tensor self,
Scalar exponent) |
static Tensor |
torch.float_power(Tensor self,
Tensor exponent) |
static Tensor |
torch.floor_(Tensor self) |
static Tensor |
torch.floor_divide_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.floor_divide_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.floor_divide(Tensor self,
Scalar other) |
static Tensor |
torch.floor_divide(Tensor self,
Tensor other) |
static Tensor |
torch.floor_out(Tensor out,
Tensor self) |
static Tensor |
torch.floor_outf(Tensor self,
Tensor out) |
static Tensor |
torch.floor(Tensor self) |
static Tensor |
torch.fmax_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.fmax_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.fmax(Tensor self,
Tensor other) |
static Tensor |
torch.fmin_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.fmin_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.fmin(Tensor self,
Tensor other) |
static Tensor |
torch.fmod_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.fmod_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.fmod_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.fmod_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.fmod(Tensor self,
Scalar other) |
static Tensor |
torch.fmod(Tensor self,
Tensor other) |
static Tensor |
torch.fold(Tensor input,
FoldOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.fold
/** about the exact behavior of this functional.
|
static Tensor |
torch.fold(Tensor input,
LongPointer output_size,
LongPointer kernel_size,
LongPointer dilation,
LongPointer padding,
LongPointer stride) |
static Tensor |
torch.frac_(Tensor self) |
static Tensor |
torch.frac_out(Tensor out,
Tensor self) |
static Tensor |
torch.frac_outf(Tensor self,
Tensor out) |
static Tensor |
torch.frac(Tensor self) |
static Tensor |
torch.fractional_max_pool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool2d_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.fractional_max_pool2d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.fractional_max_pool2d_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool2d_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices) |
static PointerPointer<Tensor> |
torch.fractional_max_pool2d_out(Tensor output,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples) |
static PointerPointer<Tensor> |
torch.fractional_max_pool2d_out(Tensor output,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples) |
static PointerPointer<Tensor> |
torch.fractional_max_pool2d_outf(Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static PointerPointer<Tensor> |
torch.fractional_max_pool2d_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static TensorTensorTuple |
torch.fractional_max_pool2d_with_indices(Tensor input,
FractionalMaxPool2dOptions options)
See the documentation for
torch::nn::functional::FractionalMaxPool2dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static TensorTensorTuple |
torch.fractional_max_pool2d_with_indices(Tensor input,
LongPointer kernel_size,
LongExpandingArrayOptional output_size,
DoubleExpandingArrayOptional output_ratio,
Tensor _random_samples) |
static Tensor |
torch.fractional_max_pool2d(Tensor input,
FractionalMaxPool2dOptions options)
See the documentation for
torch::nn::functional::FractionalMaxPool2dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static TensorTensorTuple |
torch.fractional_max_pool2d(Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples) |
static TensorTensorTuple |
torch.fractional_max_pool2d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples) |
static Tensor |
torch.fractional_max_pool2d(Tensor input,
LongPointer kernel_size,
LongExpandingArrayOptional output_size,
DoubleExpandingArrayOptional output_ratio,
Tensor _random_samples) |
static Tensor |
torch.fractional_max_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool3d_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.fractional_max_pool3d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.fractional_max_pool3d_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor indices) |
static Tensor |
torch.fractional_max_pool3d_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor indices) |
static PointerPointer<Tensor> |
torch.fractional_max_pool3d_out(Tensor output,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples) |
static PointerPointer<Tensor> |
torch.fractional_max_pool3d_out(Tensor output,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples) |
static PointerPointer<Tensor> |
torch.fractional_max_pool3d_outf(Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static PointerPointer<Tensor> |
torch.fractional_max_pool3d_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples,
Tensor output,
Tensor indices) |
static TensorTensorTuple |
torch.fractional_max_pool3d_with_indices(Tensor input,
FractionalMaxPool3dOptions options)
See the documentation for
torch::nn::functional::FractionalMaxPool3dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static TensorTensorTuple |
torch.fractional_max_pool3d_with_indices(Tensor input,
LongPointer kernel_size,
LongExpandingArrayOptional output_size,
DoubleExpandingArrayOptional output_ratio,
Tensor _random_samples) |
static Tensor |
torch.fractional_max_pool3d(Tensor input,
FractionalMaxPool3dOptions options)
See the documentation for
torch::nn::functional::FractionalMaxPool3dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static TensorTensorTuple |
torch.fractional_max_pool3d(Tensor self,
long[] kernel_size,
long[] output_size,
Tensor random_samples) |
static TensorTensorTuple |
torch.fractional_max_pool3d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef output_size,
Tensor random_samples) |
static Tensor |
torch.fractional_max_pool3d(Tensor input,
LongPointer kernel_size,
LongExpandingArrayOptional output_size,
DoubleExpandingArrayOptional output_ratio,
Tensor _random_samples) |
static PointerPointer<Tensor> |
torch.frexp_out(Tensor mantissa,
Tensor exponent,
Tensor self) |
static PointerPointer<Tensor> |
torch.frexp_outf(Tensor self,
Tensor mantissa,
Tensor exponent) |
static TensorTensorTuple |
torch.frexp(Tensor self) |
static Tensor |
torch.frobenius_norm_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.frobenius_norm_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.frobenius_norm_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.frobenius_norm_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.frobenius_norm_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.frobenius_norm_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.frobenius_norm(Tensor self) |
static Tensor |
torch.frobenius_norm(Tensor self,
long... dim) |
static Tensor |
torch.frobenius_norm(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.frobenius_norm(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.frobenius_norm(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.full_like(Tensor self,
Scalar fill_value) |
static Tensor |
torch.full_like(Tensor self,
Scalar fill_value,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.full_like(Tensor self,
Scalar fill_value,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.full_out(Tensor out,
long[] size,
Scalar fill_value) |
static Tensor |
torch.full_out(Tensor out,
LongArrayRef size,
Scalar fill_value) |
static Tensor |
torch.full_outf(long[] size,
Scalar fill_value,
Tensor out) |
static Tensor |
torch.full_outf(LongArrayRef size,
Scalar fill_value,
Tensor out) |
static Tensor |
torch.fused_moving_avg_obs_fake_quant(Tensor self,
Tensor observer_on,
Tensor fake_quant_on,
Tensor running_min,
Tensor running_max,
Tensor scale,
Tensor zero_point,
double averaging_const,
long quant_min,
long quant_max,
long ch_axis) |
static Tensor |
torch.fused_moving_avg_obs_fake_quant(Tensor self,
Tensor observer_on,
Tensor fake_quant_on,
Tensor running_min,
Tensor running_max,
Tensor scale,
Tensor zero_point,
double averaging_const,
long quant_min,
long quant_max,
long ch_axis,
boolean per_row_fake_quant,
boolean symmetric_quant) |
static Tensor |
torch.gather_backward(Tensor grad,
Tensor self,
long dim,
Tensor index,
boolean sparse_grad) |
static Tensor |
torch.gather_out(Tensor out,
Tensor self,
Dimname dim,
Tensor index) |
static Tensor |
torch.gather_out(Tensor out,
Tensor self,
Dimname dim,
Tensor index,
boolean sparse_grad) |
static Tensor |
torch.gather_out(Tensor out,
Tensor self,
long dim,
Tensor index) |
static Tensor |
torch.gather_out(Tensor out,
Tensor self,
long dim,
Tensor index,
boolean sparse_grad) |
static Tensor |
torch.gather_outf(Tensor self,
Dimname dim,
Tensor index,
boolean sparse_grad,
Tensor out) |
static Tensor |
torch.gather_outf(Tensor self,
long dim,
Tensor index,
boolean sparse_grad,
Tensor out) |
static Tensor |
torch.gather(Tensor self,
Dimname dim,
Tensor index) |
static Tensor |
torch.gather(Tensor self,
Dimname dim,
Tensor index,
boolean sparse_grad) |
static Tensor |
torch.gather(Tensor self,
long dim,
Tensor index) |
static Tensor |
torch.gather(Tensor self,
long dim,
Tensor index,
boolean sparse_grad) |
static Tensor |
torch.gcd_(Tensor self,
Tensor other) |
static Tensor |
torch.gcd_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.gcd_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.gcd(Tensor self,
Tensor other) |
static Tensor |
torch.ge_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.ge_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.ge_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.ge_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.ge(Tensor self,
Scalar other) |
static Tensor |
torch.ge(Tensor self,
Tensor other) |
static Tensor |
torch.gelu_backward_out(Tensor grad_input,
Tensor grad,
Tensor self) |
static Tensor |
torch.gelu_backward_outf(Tensor grad,
Tensor self,
Tensor grad_input) |
static Tensor |
torch.gelu_backward(Tensor grad,
Tensor self) |
static Tensor |
torch.gelu_out(Tensor out,
Tensor self) |
static Tensor |
torch.gelu_outf(Tensor self,
Tensor out) |
static Tensor |
torch.gelu(Tensor self) |
static PointerPointer<Tensor> |
torch.geqrf_out(Tensor a,
Tensor tau,
Tensor self) |
static PointerPointer<Tensor> |
torch.geqrf_outf(Tensor self,
Tensor a,
Tensor tau) |
static TensorTensorTuple |
torch.geqrf(Tensor self) |
static Tensor |
torch.ger_out(Tensor out,
Tensor self,
Tensor vec2) |
static Tensor |
torch.ger_outf(Tensor self,
Tensor vec2,
Tensor out) |
static Tensor |
torch.ger(Tensor self,
Tensor vec2) |
static Tensor |
torch.get_item(Tensor self,
TensorIndexArrayRef indices) |
static Tensor |
torch.get_item(Tensor self,
TensorIndexArrayRef indices,
boolean disable_slice_optimization) |
static TensorBase |
torch.get_tensor_base(Tensor t) |
static Tensor |
torch.getSizeOf(Tensor var,
long dim) |
static WriteableTensorData |
torch.getWriteableTensorData(Tensor tensor) |
static WriteableTensorData |
torch.getWriteableTensorData(Tensor tensor,
boolean to_cpu) |
static Tensor |
torch.glu_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long dim) |
static Tensor |
torch.glu_backward_outf(Tensor grad_output,
Tensor self,
long dim,
Tensor grad_input) |
static Tensor |
torch.glu_backward(Tensor grad_output,
Tensor self,
long dim) |
static Tensor |
torch.glu_out(Tensor out,
Tensor self) |
static Tensor |
torch.glu_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.glu_outf(Tensor self,
long dim,
Tensor out) |
static Tensor |
torch.glu(Tensor self) |
static Tensor |
torch.glu(Tensor input,
GLUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.glu
/** about the exact behavior of this functional.
|
static Tensor |
torch.glu(Tensor self,
long dim) |
static Node |
torch.grad_accumulator(Tensor arg0)
Gets the gradient accumulator of the
Variable if it has one, or else
create one on the fly and return it. |
static Node |
torch.grad_fn_unsafe(Tensor arg0)
Gets the raw gradient function pointer, whatever it currently is.
|
static Edge |
torch.gradient_edge(Tensor arg0)
Returns the "canonical" gradient edge of this
Variable, i.e. |
static TensorVector |
torch.gradient(Tensor self) |
static TensorVector |
torch.gradient(Tensor self,
long... dim) |
static TensorVector |
torch.gradient(Tensor self,
long[] dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
LongArrayRef dim) |
static TensorVector |
torch.gradient(Tensor self,
LongArrayRef dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing,
long... dim) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing,
long[] dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing,
LongArrayRef dim) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing,
LongArrayRef dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
ScalarArrayRef spacing,
LongOptional dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
Scalar spacing,
long... dim) |
static TensorVector |
torch.gradient(Tensor self,
Scalar spacing,
long[] dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
Scalar spacing,
LongArrayRef dim) |
static TensorVector |
torch.gradient(Tensor self,
Scalar spacing,
LongArrayRef dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
ScalarOptional spacing,
LongOptional dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
TensorArrayRef spacing) |
static TensorVector |
torch.gradient(Tensor self,
TensorArrayRef spacing,
long... dim) |
static TensorVector |
torch.gradient(Tensor self,
TensorArrayRef spacing,
long[] dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
TensorArrayRef spacing,
LongArrayRef dim) |
static TensorVector |
torch.gradient(Tensor self,
TensorArrayRef spacing,
LongArrayRef dim,
long edge_order) |
static TensorVector |
torch.gradient(Tensor self,
TensorArrayRef spacing,
LongOptional dim,
long edge_order) |
static Tensor |
torch.greater_equal_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.greater_equal_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.greater_equal_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.greater_equal_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.greater_equal(Tensor self,
Scalar other) |
static Tensor |
torch.greater_equal(Tensor self,
Tensor other) |
static Tensor |
torch.greater_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.greater_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.greater_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.greater_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.greater(Tensor self,
Scalar other) |
static Tensor |
torch.greater(Tensor self,
Tensor other) |
static Tensor |
torch.greaterThan(Scalar x,
Tensor y) |
static Tensor |
torch.greaterThan(Tensor x,
Scalar y) |
static Tensor |
torch.greaterThan(Tensor x,
Tensor y) |
static Tensor |
torch.greaterThanEquals(Scalar x,
Tensor y) |
static Tensor |
torch.greaterThanEquals(Tensor x,
Scalar y) |
static Tensor |
torch.greaterThanEquals(Tensor x,
Tensor y) |
static Tensor |
torch.grid_sample(Tensor input,
Tensor grid) |
static Tensor |
torch.grid_sample(Tensor input,
Tensor grid,
grid_sample_mode_t mode,
grid_sample_padding_mode_t padding_mode,
BoolOptional align_corners) |
static Tensor |
torch.grid_sample(Tensor input,
Tensor grid,
GridSampleFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.grid_sample
/** about the exact behavior of this functional.
|
static TensorTensorTuple |
torch.grid_sampler_2d_backward(Tensor grad_output,
Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static Tensor |
torch.grid_sampler_2d(Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static TensorTensorTuple |
torch.grid_sampler_3d_backward(Tensor grad_output,
Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static Tensor |
torch.grid_sampler_3d(Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static Tensor |
torch.grid_sampler(Tensor input,
Tensor grid,
long interpolation_mode,
long padding_mode,
boolean align_corners) |
static Tensor |
torch.group_norm(Tensor input,
GroupNormFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.group_norm
/** about the exact behavior of this functional.
|
static Tensor |
torch.group_norm(Tensor input,
long num_groups) |
static Tensor |
torch.group_norm(Tensor input,
long num_groups,
TensorOptional weight,
TensorOptional bias,
double eps,
boolean cudnn_enabled) |
static Tensor |
torch.group_norm(Tensor input,
long num_groups,
Tensor weight,
Tensor bias,
double eps) |
static Tensor |
torch.gru_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh) |
static Tensor |
torch.gru_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static TensorTensorTuple |
torch.gru(Tensor input,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static TensorTensorTuple |
torch.gru(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static Tensor |
torch.gt_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.gt_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.gt_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.gt_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.gt(Tensor self,
Scalar other) |
static Tensor |
torch.gt(Tensor self,
Tensor other) |
static Tensor |
torch.gumbel_softmax(Tensor logits) |
static Tensor |
torch.gumbel_softmax(Tensor logits,
double tau,
boolean hard,
int dim) |
static Tensor |
torch.gumbel_softmax(Tensor logits,
GumbelSoftmaxFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.gumbel_softmax
/** about the exact behavior of this functional.
|
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
long[] dim_ptr,
long[] specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
long... prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
long[] dim_ptr,
long[] specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
LongArrayRef prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
LongBuffer dim_ptr,
LongBuffer specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
long... prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
LongBuffer dim_ptr,
LongBuffer specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
LongArrayRef prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
LongPointer dim_ptr,
LongPointer specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
long... prev_dim_result_sizes) |
static Tensor |
torch.handleDimInMultiDimIndexing(Tensor prev_dim_result,
Tensor original_tensor,
TensorIndex index,
LongPointer dim_ptr,
LongPointer specified_dims_ptr,
long real_dim,
TensorVector outIndices,
boolean disable_slice_optimization,
Device original_tensor_device,
LongArrayRef prev_dim_result_sizes) |
static Tensor |
torch.hardshrink_backward_out(Tensor grad_input,
Tensor grad_out,
Tensor self,
Scalar lambd) |
static Tensor |
torch.hardshrink_backward_outf(Tensor grad_out,
Tensor self,
Scalar lambd,
Tensor grad_input) |
static Tensor |
torch.hardshrink_backward(Tensor grad_out,
Tensor self,
Scalar lambd) |
static Tensor |
torch.hardshrink_out(Tensor out,
Tensor self) |
static Tensor |
torch.hardshrink_out(Tensor out,
Tensor self,
Scalar lambd) |
static Tensor |
torch.hardshrink_outf(Tensor self,
Scalar lambd,
Tensor out) |
static Tensor |
torch.hardshrink(Tensor self) |
static Tensor |
torch.hardshrink(Tensor input,
double lambda) |
static Tensor |
torch.hardshrink(Tensor input,
HardshrinkOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.hardshrink
/** about the exact behavior of this functional.
|
static Tensor |
torch.hardshrink(Tensor self,
Scalar lambd) |
static Tensor |
torch.hardsigmoid_(Tensor self) |
static Tensor |
torch.hardsigmoid_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self) |
static Tensor |
torch.hardsigmoid_backward_outf(Tensor grad_output,
Tensor self,
Tensor grad_input) |
static Tensor |
torch.hardsigmoid_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.hardsigmoid_out(Tensor out,
Tensor self) |
static Tensor |
torch.hardsigmoid_outf(Tensor self,
Tensor out) |
static Tensor |
torch.hardsigmoid(Tensor self) |
static Tensor |
torch.hardswish_(Tensor self) |
static Tensor |
torch.hardswish_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.hardswish_out(Tensor out,
Tensor self) |
static Tensor |
torch.hardswish_outf(Tensor self,
Tensor out) |
static Tensor |
torch.hardswish(Tensor self) |
static Tensor |
torch.hardtanh_(Tensor self) |
static Tensor |
torch.hardtanh_(Tensor self,
Scalar min_val,
Scalar max_val) |
static Tensor |
torch.hardtanh_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Scalar min_val,
Scalar max_val) |
static Tensor |
torch.hardtanh_backward_outf(Tensor grad_output,
Tensor self,
Scalar min_val,
Scalar max_val,
Tensor grad_input) |
static Tensor |
torch.hardtanh_backward(Tensor grad_output,
Tensor self,
Scalar min_val,
Scalar max_val) |
static Tensor |
torch.hardtanh_out(Tensor out,
Tensor self) |
static Tensor |
torch.hardtanh_out(Tensor out,
Tensor self,
Scalar min_val,
Scalar max_val) |
static Tensor |
torch.hardtanh_outf(Tensor self,
Scalar min_val,
Scalar max_val,
Tensor out) |
static Tensor |
torch.hardtanh(Tensor self) |
static Tensor |
torch.hardtanh(Tensor input,
double min_val,
double max_val,
boolean inplace) |
static Tensor |
torch.hardtanh(Tensor input,
HardtanhOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.hardtanh
/** about the exact behavior of this functional.
|
static Tensor |
torch.hardtanh(Tensor self,
Scalar min_val,
Scalar max_val) |
static Tensor |
torch.heaviside_out(Tensor out,
Tensor self,
Tensor values) |
static Tensor |
torch.heaviside_outf(Tensor self,
Tensor values,
Tensor out) |
static Tensor |
torch.heaviside(Tensor self,
Tensor values) |
static Tensor |
torch.hinge_embedding_loss(Tensor self,
Tensor target) |
static Tensor |
torch.hinge_embedding_loss(Tensor self,
Tensor target,
double margin,
long reduction) |
static Tensor |
torch.hinge_embedding_loss(Tensor input,
Tensor target,
double margin,
loss_reduction_t reduction) |
static Tensor |
torch.hinge_embedding_loss(Tensor input,
Tensor target,
HingeEmbeddingLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.hinge_embedding_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.histc_out(Tensor out,
Tensor self) |
static Tensor |
torch.histc_out(Tensor out,
Tensor self,
long bins,
Scalar min,
Scalar max) |
static Tensor |
torch.histc_outf(Tensor self,
long bins,
Scalar min,
Scalar max,
Tensor out) |
static Tensor |
torch.histc(Tensor self) |
static Tensor |
torch.histc(Tensor self,
long bins,
Scalar min,
Scalar max) |
static PointerPointer<Tensor> |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self) |
static PointerPointer<Tensor> |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self,
long bins,
DoubleArrayRefOptional range,
TensorOptional weight,
boolean density) |
static PointerPointer<Tensor> |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self,
Tensor bins) |
static PointerPointer<Tensor> |
torch.histogram_out(Tensor hist,
Tensor bin_edges,
Tensor self,
Tensor bins,
TensorOptional weight,
boolean density) |
static PointerPointer<Tensor> |
torch.histogram_outf(Tensor self,
long bins,
DoubleArrayRefOptional range,
TensorOptional weight,
boolean density,
Tensor hist,
Tensor bin_edges) |
static PointerPointer<Tensor> |
torch.histogram_outf(Tensor self,
Tensor bins,
TensorOptional weight,
boolean density,
Tensor hist,
Tensor bin_edges) |
static TensorTensorTuple |
torch.histogram(Tensor self) |
static TensorTensorTuple |
torch.histogram(Tensor self,
long bins,
DoubleArrayRefOptional range,
TensorOptional weight,
boolean density) |
static TensorTensorTuple |
torch.histogram(Tensor self,
Tensor bins) |
static TensorTensorTuple |
torch.histogram(Tensor self,
Tensor bins,
TensorOptional weight,
boolean density) |
static FunctionPreVector |
torch.hooks(Tensor arg0) |
static TensorVector |
torch.hsplit(Tensor self,
long... indices) |
static TensorVector |
torch.hsplit(Tensor self,
long sections) |
static TensorVector |
torch.hsplit(Tensor self,
LongArrayRef indices) |
static Tensor |
torch.hspmm_out(Tensor out,
Tensor mat1,
Tensor mat2) |
static Tensor |
torch.hspmm_outf(Tensor mat1,
Tensor mat2,
Tensor out) |
static Tensor |
torch.hspmm(Tensor mat1,
Tensor mat2) |
static Tensor |
torch.hstack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.hstack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.huber_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double delta) |
static Tensor |
torch.huber_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double delta,
Tensor grad_input) |
static Tensor |
torch.huber_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double delta) |
static Tensor |
torch.huber_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.huber_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction,
double delta) |
static Tensor |
torch.huber_loss_outf(Tensor self,
Tensor target,
long reduction,
double delta,
Tensor out) |
static Tensor |
torch.huber_loss(Tensor self,
Tensor target) |
static Tensor |
torch.huber_loss(Tensor input,
Tensor target,
HuberLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.huber_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.huber_loss(Tensor self,
Tensor target,
long reduction,
double delta) |
static Tensor |
torch.huber_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.huber_loss(Tensor input,
Tensor target,
loss_reduction_t reduction,
double delta) |
static Tensor |
torch.hypot_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.hypot_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.hypot(Tensor self,
Tensor other) |
static Tensor |
torch.i0_(Tensor self) |
static Tensor |
torch.i0_out(Tensor out,
Tensor self) |
static Tensor |
torch.i0_outf(Tensor self,
Tensor out) |
static Tensor |
torch.i0(Tensor self) |
static Tensor |
torch.igamma_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.igamma_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.igamma(Tensor self,
Tensor other) |
static Tensor |
torch.igammac_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.igammac_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.igammac(Tensor self,
Tensor other) |
static Tensor |
torch.im2col_backward_out(Tensor grad_input,
Tensor grad_output,
long[] input_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.im2col_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef input_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.im2col_backward_outf(Tensor grad_output,
long[] input_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long[] stride,
Tensor grad_input) |
static Tensor |
torch.im2col_backward_outf(Tensor grad_output,
LongArrayRef input_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride,
Tensor grad_input) |
static Tensor |
torch.im2col_backward(Tensor grad_output,
long[] input_size,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.im2col_backward(Tensor grad_output,
LongArrayRef input_size,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.im2col_out(Tensor out,
Tensor self,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.im2col_out(Tensor out,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.im2col_outf(Tensor self,
long[] kernel_size,
long[] dilation,
long[] padding,
long[] stride,
Tensor out) |
static Tensor |
torch.im2col_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride,
Tensor out) |
static Tensor |
torch.im2col(Tensor self,
long[] kernel_size,
long[] dilation,
long[] padding,
long... stride) |
static Tensor |
torch.im2col(Tensor self,
LongArrayRef kernel_size,
LongArrayRef dilation,
LongArrayRef padding,
LongArrayRef stride) |
static Tensor |
torch.imag(Tensor self) |
static Tensor |
torch.index_add(Tensor self,
Dimname dim,
Tensor index,
Tensor source) |
static Tensor |
torch.index_add(Tensor self,
Dimname dim,
Tensor index,
Tensor source,
Scalar alpha) |
static Tensor |
torch.index_add(Tensor self,
long dim,
Tensor index,
Tensor source) |
static Tensor |
torch.index_add(Tensor self,
long dim,
Tensor index,
Tensor source,
Scalar alpha) |
static Tensor |
torch.index_copy(Tensor self,
Dimname dim,
Tensor index,
Tensor source) |
static Tensor |
torch.index_copy(Tensor self,
long dim,
Tensor index,
Tensor source) |
static Tensor |
torch.index_fill(Tensor self,
Dimname dim,
Tensor index,
Scalar value) |
static Tensor |
torch.index_fill(Tensor self,
Dimname dim,
Tensor index,
Tensor value) |
static Tensor |
torch.index_fill(Tensor self,
long dim,
Tensor index,
Scalar value) |
static Tensor |
torch.index_fill(Tensor self,
long dim,
Tensor index,
Tensor value) |
static Tensor |
torch.index_select_backward(Tensor grad,
long[] self_sizes,
long dim,
Tensor index) |
static Tensor |
torch.index_select_backward(Tensor grad,
LongArrayRef self_sizes,
long dim,
Tensor index) |
static Tensor |
torch.index_select_out(Tensor out,
Tensor self,
Dimname dim,
Tensor index) |
static Tensor |
torch.index_select_out(Tensor out,
Tensor self,
long dim,
Tensor index) |
static Tensor |
torch.index_select_outf(Tensor self,
Dimname dim,
Tensor index,
Tensor out) |
static Tensor |
torch.index_select_outf(Tensor self,
long dim,
Tensor index,
Tensor out) |
static Tensor |
torch.index_select(Tensor self,
Dimname dim,
Tensor index) |
static Tensor |
torch.index_select(Tensor self,
long dim,
Tensor index) |
static Tensor |
torch.infinitely_differentiable_gelu_backward(Tensor grad,
Tensor self) |
static Tensor |
torch.inner_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.inner_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.inner(Tensor self,
Tensor other) |
static Tensor |
torch.instance_norm(Tensor input) |
static Tensor |
torch.instance_norm(Tensor input,
InstanceNormFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.instance_norm
/** about the exact behavior of this functional.
|
static Tensor |
torch.instance_norm(Tensor input,
TensorOptional weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean use_input_stats,
double momentum,
double eps,
boolean cudnn_enabled) |
static Tensor |
torch.instance_norm(Tensor input,
Tensor running_mean,
Tensor running_var,
Tensor weight,
Tensor bias,
boolean use_input_stats,
double momentum,
double eps) |
static Tensor |
torch.int_repr(Tensor self) |
static Tensor |
torch.interpolate(Tensor input) |
static Tensor |
torch.interpolate(Tensor input,
InterpolateFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.interpolate
/** about the exact behavior of this functional.
|
static Tensor |
torch.interpolate(Tensor input,
LongVectorOptional size,
DoubleVectorOptional scale_factor,
interpolate_mode_t mode,
BoolOptional align_corners,
BoolOptional recompute_scale_factor) |
static Tensor |
torch.inverse_out(Tensor out,
Tensor self) |
static Tensor |
torch.inverse_outf(Tensor self,
Tensor out) |
static Tensor |
torch.inverse(Tensor self) |
static Tensor |
torch.invert_permutation(Tensor permutation) |
static boolean |
torch.is_complex(Tensor tensor) |
static boolean |
torch.is_conj(Tensor tensor) |
static boolean |
torch.is_distributed(Tensor self) |
static boolean |
torch.is_floating_point(Tensor tensor) |
static boolean |
torch.is_inference(Tensor tensor) |
static boolean |
torch.is_neg(Tensor tensor) |
static boolean |
torch.is_nonzero(Tensor self) |
static boolean |
torch.is_same_size(Tensor self,
Tensor other) |
static boolean |
torch.is_signed(Tensor tensor) |
static Tensor |
torch.isclose(Tensor self,
Tensor other) |
static Tensor |
torch.isclose(Tensor self,
Tensor other,
double rtol,
double atol,
boolean equal_nan) |
static Tensor |
torch.isfinite(Tensor self) |
static Tensor |
torch.isin_out(Tensor out,
Scalar element,
Tensor test_elements) |
static Tensor |
torch.isin_out(Tensor out,
Scalar element,
Tensor test_elements,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isin_out(Tensor out,
Tensor elements,
Scalar test_element) |
static Tensor |
torch.isin_out(Tensor out,
Tensor elements,
Scalar test_element,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isin_out(Tensor out,
Tensor elements,
Tensor test_elements) |
static Tensor |
torch.isin_out(Tensor out,
Tensor elements,
Tensor test_elements,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isin_outf(Scalar element,
Tensor test_elements,
boolean assume_unique,
boolean invert,
Tensor out) |
static Tensor |
torch.isin_outf(Tensor elements,
Scalar test_element,
boolean assume_unique,
boolean invert,
Tensor out) |
static Tensor |
torch.isin_outf(Tensor elements,
Tensor test_elements,
boolean assume_unique,
boolean invert,
Tensor out) |
static Tensor |
torch.isin(Scalar element,
Tensor test_elements) |
static Tensor |
torch.isin(Scalar element,
Tensor test_elements,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isin(Tensor elements,
Scalar test_element) |
static Tensor |
torch.isin(Tensor elements,
Scalar test_element,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isin(Tensor elements,
Tensor test_elements) |
static Tensor |
torch.isin(Tensor elements,
Tensor test_elements,
boolean assume_unique,
boolean invert) |
static Tensor |
torch.isinf(Tensor self) |
static Tensor |
torch.isnan(Tensor self) |
static Tensor |
torch.isneginf_out(Tensor out,
Tensor self) |
static Tensor |
torch.isneginf_outf(Tensor self,
Tensor out) |
static Tensor |
torch.isneginf(Tensor self) |
static Tensor |
torch.isposinf_out(Tensor out,
Tensor self) |
static Tensor |
torch.isposinf_outf(Tensor self,
Tensor out) |
static Tensor |
torch.isposinf(Tensor self) |
static Tensor |
torch.isreal(Tensor self) |
static Tensor |
torch.istft(Tensor self,
long n_fft) |
static Tensor |
torch.istft(Tensor self,
long n_fft,
LongOptional hop_length,
LongOptional win_length,
TensorOptional window,
boolean center,
boolean normalized,
BoolOptional onesided,
LongOptional length,
boolean return_complex) |
static Tensor |
torch.kaiming_normal_(Tensor tensor) |
static Tensor |
torch.kaiming_normal_(Tensor tensor,
double a,
FanModeType mode,
NonlinearityType nonlinearity)
Fills the input
Tensor with values according to the method
described in "Delving deep into rectifiers: Surpassing human-level
performance on ImageNet classification" - He, K. |
static Tensor |
torch.kaiming_uniform_(Tensor tensor) |
static Tensor |
torch.kaiming_uniform_(Tensor tensor,
double a,
FanModeType mode,
NonlinearityType nonlinearity)
Fills the input
Tensor with values according to the method
described in "Delving deep into rectifiers: Surpassing human-level
performance on ImageNet classification" - He, K. |
static Tensor |
torch.kl_div_backward(Tensor grad_output,
Tensor self,
Tensor target) |
static Tensor |
torch.kl_div_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
boolean log_target) |
static Tensor |
torch.kl_div(Tensor self,
Tensor target) |
static Tensor |
torch.kl_div(Tensor input,
Tensor target,
kldiv_loss_reduction_t reduction) |
static Tensor |
torch.kl_div(Tensor input,
Tensor target,
kldiv_loss_reduction_t reduction,
boolean log_target) |
static Tensor |
torch.kl_div(Tensor input,
Tensor target,
KLDivLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.kl_div
/** about the exact behavior of this functional.
|
static Tensor |
torch.kl_div(Tensor self,
Tensor target,
long reduction,
boolean log_target) |
static Tensor |
torch.kron_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.kron_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.kron(Tensor self,
Tensor other) |
static PointerPointer<Tensor> |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k) |
static PointerPointer<Tensor> |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k,
Dimname dim) |
static PointerPointer<Tensor> |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.kthvalue_out(Tensor values,
Tensor indices,
Tensor self,
long k,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.kthvalue_outf(Tensor self,
long k,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.kthvalue_outf(Tensor self,
long k,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static TensorTensorTuple |
torch.kthvalue(Tensor self,
long k) |
static TensorTensorTuple |
torch.kthvalue(Tensor self,
long k,
Dimname dim) |
static TensorTensorTuple |
torch.kthvalue(Tensor self,
long k,
Dimname dim,
boolean keepdim) |
static TensorTensorTuple |
torch.kthvalue(Tensor self,
long k,
long dim,
boolean keepdim) |
static Tensor |
torch.l1_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.l1_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor grad_input) |
static Tensor |
torch.l1_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.l1_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.l1_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.l1_loss_outf(Tensor self,
Tensor target,
long reduction,
Tensor out) |
static Tensor |
torch.l1_loss(Tensor self,
Tensor target) |
static Tensor |
torch.l1_loss(Tensor input,
Tensor target,
L1LossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.l1_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.l1_loss(Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.l1_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.layer_norm(Tensor input,
LayerNormFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.layer_norm
/** about the exact behavior of this functional.
|
static Tensor |
torch.layer_norm(Tensor input,
long... normalized_shape) |
static Tensor |
torch.layer_norm(Tensor input,
long[] normalized_shape,
TensorOptional weight,
TensorOptional bias,
double eps,
boolean cudnn_enable) |
static Tensor |
torch.layer_norm(Tensor input,
LongArrayRef normalized_shape) |
static Tensor |
torch.layer_norm(Tensor input,
LongArrayRef normalized_shape,
TensorOptional weight,
TensorOptional bias,
double eps,
boolean cudnn_enable) |
static Tensor |
torch.layer_norm(Tensor input,
LongVector normalized_shape,
Tensor weight,
Tensor bias,
double eps) |
static Tensor |
torch.lcm_(Tensor self,
Tensor other) |
static Tensor |
torch.lcm_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.lcm_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.lcm(Tensor self,
Tensor other) |
static Tensor |
torch.ldexp_(Tensor self,
Tensor other) |
static Tensor |
torch.ldexp_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.ldexp_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.ldexp(Tensor self,
Tensor other) |
static Tensor |
torch.le_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.le_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.le_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.le_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.le(Tensor self,
Scalar other) |
static Tensor |
torch.le(Tensor self,
Tensor other) |
static Tensor |
torch.leaky_relu_(Tensor self) |
static Tensor |
torch.leaky_relu_(Tensor self,
Scalar negative_slope) |
static Tensor |
torch.leaky_relu_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Scalar negative_slope,
boolean self_is_result) |
static Tensor |
torch.leaky_relu_backward_outf(Tensor grad_output,
Tensor self,
Scalar negative_slope,
boolean self_is_result,
Tensor grad_input) |
static Tensor |
torch.leaky_relu_backward(Tensor grad_output,
Tensor self,
Scalar negative_slope,
boolean self_is_result) |
static Tensor |
torch.leaky_relu_out(Tensor out,
Tensor self) |
static Tensor |
torch.leaky_relu_out(Tensor out,
Tensor self,
Scalar negative_slope) |
static Tensor |
torch.leaky_relu_outf(Tensor self,
Scalar negative_slope,
Tensor out) |
static Tensor |
torch.leaky_relu(Tensor self) |
static Tensor |
torch.leaky_relu(Tensor input,
double negative_slope,
boolean inplace) |
static Tensor |
torch.leaky_relu(Tensor input,
LeakyReLUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.leaky_relu
/** about the exact behavior of this functional.
|
static Tensor |
torch.leaky_relu(Tensor self,
Scalar negative_slope) |
static Tensor |
torch.lerp_out(Tensor out,
Tensor self,
Tensor end,
Scalar weight) |
static Tensor |
torch.lerp_out(Tensor out,
Tensor self,
Tensor end,
Tensor weight) |
static Tensor |
torch.lerp_outf(Tensor self,
Tensor end,
Scalar weight,
Tensor out) |
static Tensor |
torch.lerp_outf(Tensor self,
Tensor end,
Tensor weight,
Tensor out) |
static Tensor |
torch.lerp(Tensor self,
Tensor end,
Scalar weight) |
static Tensor |
torch.lerp(Tensor self,
Tensor end,
Tensor weight) |
static Tensor |
torch.less_equal_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.less_equal_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.less_equal_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.less_equal_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.less_equal(Tensor self,
Scalar other) |
static Tensor |
torch.less_equal(Tensor self,
Tensor other) |
static Tensor |
torch.less_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.less_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.less_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.less_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.less(Tensor self,
Scalar other) |
static Tensor |
torch.less(Tensor self,
Tensor other) |
static Tensor |
torch.lessThan(Scalar x,
Tensor y) |
static Tensor |
torch.lessThan(Tensor x,
Scalar y) |
static Tensor |
torch.lessThan(Tensor x,
Tensor y) |
static Tensor |
torch.lessThanEquals(Scalar x,
Tensor y) |
static Tensor |
torch.lessThanEquals(Tensor x,
Scalar y) |
static Tensor |
torch.lessThanEquals(Tensor x,
Tensor y) |
static Tensor |
torch.lgamma_out(Tensor out,
Tensor self) |
static Tensor |
torch.lgamma_outf(Tensor self,
Tensor out) |
static Tensor |
torch.lgamma(Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_cholesky_ex_out(Tensor L,
Tensor info,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_cholesky_ex_out(Tensor L,
Tensor info,
Tensor self,
boolean upper,
boolean check_errors) |
static PointerPointer<Tensor> |
torch.linalg_cholesky_ex_outf(Tensor self,
boolean upper,
boolean check_errors,
Tensor L,
Tensor info) |
static TensorTensorTuple |
torch.linalg_cholesky_ex(Tensor self) |
static TensorTensorTuple |
torch.linalg_cholesky_ex(Tensor self,
boolean upper,
boolean check_errors) |
static Tensor |
torch.linalg_cholesky_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_cholesky_out(Tensor out,
Tensor self,
boolean upper) |
static Tensor |
torch.linalg_cholesky_outf(Tensor self,
boolean upper,
Tensor out) |
static Tensor |
torch.linalg_cholesky(Tensor self) |
static Tensor |
torch.linalg_cholesky(Tensor self,
boolean upper) |
static Tensor |
torch.linalg_cond_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_cond_out(Tensor out,
Tensor self,
Pointer p) |
static Tensor |
torch.linalg_cond_out(Tensor out,
Tensor self,
ScalarOptional p) |
static Tensor |
torch.linalg_cond_outf(Tensor self,
Pointer p,
Tensor out) |
static Tensor |
torch.linalg_cond_outf(Tensor self,
ScalarOptional p,
Tensor out) |
static Tensor |
torch.linalg_cond(Tensor self) |
static Tensor |
torch.linalg_cond(Tensor self,
Pointer p) |
static Tensor |
torch.linalg_cond(Tensor self,
ScalarOptional p) |
static Tensor |
torch.linalg_det_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_det_outf(Tensor self,
Tensor out) |
static Tensor |
torch.linalg_det(Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_eig_out(Tensor eigenvalues,
Tensor eigenvectors,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_eig_outf(Tensor self,
Tensor eigenvalues,
Tensor eigenvectors) |
static TensorTensorTuple |
torch.linalg_eig(Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_eigh_out(Tensor eigvals,
Tensor eigvecs,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_eigh_out(Tensor eigvals,
Tensor eigvecs,
Tensor self,
Pointer UPLO) |
static PointerPointer<Tensor> |
torch.linalg_eigh_outf(Tensor self,
Pointer UPLO,
Tensor eigvals,
Tensor eigvecs) |
static TensorTensorTuple |
torch.linalg_eigh(Tensor self) |
static TensorTensorTuple |
torch.linalg_eigh(Tensor self,
Pointer UPLO) |
static Tensor |
torch.linalg_eigvals_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_eigvals_outf(Tensor self,
Tensor out) |
static Tensor |
torch.linalg_eigvals(Tensor self) |
static Tensor |
torch.linalg_eigvalsh_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_eigvalsh_out(Tensor out,
Tensor self,
Pointer UPLO) |
static Tensor |
torch.linalg_eigvalsh_outf(Tensor self,
Pointer UPLO,
Tensor out) |
static Tensor |
torch.linalg_eigvalsh(Tensor self) |
static Tensor |
torch.linalg_eigvalsh(Tensor self,
Pointer UPLO) |
static Tensor |
torch.linalg_householder_product_out(Tensor out,
Tensor input,
Tensor tau) |
static Tensor |
torch.linalg_householder_product_outf(Tensor input,
Tensor tau,
Tensor out) |
static Tensor |
torch.linalg_householder_product(Tensor input,
Tensor tau) |
static PointerPointer<Tensor> |
torch.linalg_inv_ex_out(Tensor inverse,
Tensor info,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_inv_ex_out(Tensor inverse,
Tensor info,
Tensor self,
boolean check_errors) |
static PointerPointer<Tensor> |
torch.linalg_inv_ex_outf(Tensor self,
boolean check_errors,
Tensor inverse,
Tensor info) |
static TensorTensorTuple |
torch.linalg_inv_ex(Tensor self) |
static TensorTensorTuple |
torch.linalg_inv_ex(Tensor self,
boolean check_errors) |
static Tensor |
torch.linalg_inv_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_inv_outf(Tensor self,
Tensor out) |
static Tensor |
torch.linalg_inv(Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_lstsq_out(Tensor solution,
Tensor residuals,
Tensor rank,
Tensor singular_values,
Tensor self,
Tensor b) |
static PointerPointer<Tensor> |
torch.linalg_lstsq_out(Tensor solution,
Tensor residuals,
Tensor rank,
Tensor singular_values,
Tensor self,
Tensor b,
DoubleOptional rcond,
Pointer driver) |
static PointerPointer<Tensor> |
torch.linalg_lstsq_outf(Tensor self,
Tensor b,
DoubleOptional rcond,
Pointer driver,
Tensor solution,
Tensor residuals,
Tensor rank,
Tensor singular_values) |
static TensorTensorTensorTensorTuple |
torch.linalg_lstsq(Tensor self,
Tensor b) |
static TensorTensorTensorTensorTuple |
torch.linalg_lstsq(Tensor self,
Tensor b,
DoubleOptional rcond,
Pointer driver) |
static Tensor |
torch.linalg_matmul_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.linalg_matmul_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.linalg_matmul(Tensor self,
Tensor other) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self,
Pointer ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self,
Pointer ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self,
Scalar ord) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self,
Scalar ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm_out(Tensor out,
Tensor self,
Scalar ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm_outf(Tensor self,
Pointer ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_matrix_norm_outf(Tensor self,
Pointer ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_matrix_norm_outf(Tensor self,
Scalar ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_matrix_norm_outf(Tensor self,
Scalar ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_matrix_norm(Tensor self) |
static Tensor |
torch.linalg_matrix_norm(Tensor self,
Pointer ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm(Tensor self,
Pointer ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm(Tensor self,
Scalar ord) |
static Tensor |
torch.linalg_matrix_norm(Tensor self,
Scalar ord,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_norm(Tensor self,
Scalar ord,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_matrix_power_out(Tensor out,
Tensor self,
long n) |
static Tensor |
torch.linalg_matrix_power_outf(Tensor self,
long n,
Tensor out) |
static Tensor |
torch.linalg_matrix_power(Tensor self,
long n) |
static Tensor |
torch.linalg_matrix_rank_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_matrix_rank_out(Tensor out,
Tensor self,
DoubleOptional tol,
boolean hermitian) |
static Tensor |
torch.linalg_matrix_rank_out(Tensor out,
Tensor input,
Tensor tol) |
static Tensor |
torch.linalg_matrix_rank_out(Tensor out,
Tensor input,
Tensor tol,
boolean hermitian) |
static Tensor |
torch.linalg_matrix_rank_outf(Tensor self,
DoubleOptional tol,
boolean hermitian,
Tensor out) |
static Tensor |
torch.linalg_matrix_rank_outf(Tensor input,
Tensor tol,
boolean hermitian,
Tensor out) |
static Tensor |
torch.linalg_matrix_rank(Tensor self) |
static Tensor |
torch.linalg_matrix_rank(Tensor self,
DoubleOptional tol,
boolean hermitian) |
static Tensor |
torch.linalg_matrix_rank(Tensor input,
Tensor tol) |
static Tensor |
torch.linalg_matrix_rank(Tensor input,
Tensor tol,
boolean hermitian) |
static Tensor |
torch.linalg_multi_dot_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.linalg_multi_dot_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.linalg_norm_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_norm_out(Tensor out,
Tensor self,
Pointer ord) |
static Tensor |
torch.linalg_norm_out(Tensor out,
Tensor self,
Pointer ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_norm_out(Tensor out,
Tensor self,
ScalarOptional ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_norm_outf(Tensor self,
Pointer ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_norm_outf(Tensor self,
ScalarOptional ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_norm(Tensor self) |
static Tensor |
torch.linalg_norm(Tensor self,
Pointer ord) |
static Tensor |
torch.linalg_norm(Tensor self,
Pointer ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_norm(Tensor self,
ScalarOptional ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_pinv_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_pinv_out(Tensor out,
Tensor self,
double rcond,
boolean hermitian) |
static Tensor |
torch.linalg_pinv_out(Tensor out,
Tensor self,
Tensor rcond) |
static Tensor |
torch.linalg_pinv_out(Tensor out,
Tensor self,
Tensor rcond,
boolean hermitian) |
static Tensor |
torch.linalg_pinv_outf(Tensor self,
double rcond,
boolean hermitian,
Tensor out) |
static Tensor |
torch.linalg_pinv_outf(Tensor self,
Tensor rcond,
boolean hermitian,
Tensor out) |
static Tensor |
torch.linalg_pinv(Tensor self) |
static Tensor |
torch.linalg_pinv(Tensor self,
double rcond,
boolean hermitian) |
static Tensor |
torch.linalg_pinv(Tensor self,
Tensor rcond) |
static Tensor |
torch.linalg_pinv(Tensor self,
Tensor rcond,
boolean hermitian) |
static PointerPointer<Tensor> |
torch.linalg_qr_out(Tensor Q,
Tensor R,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_qr_out(Tensor Q,
Tensor R,
Tensor self,
Pointer mode) |
static PointerPointer<Tensor> |
torch.linalg_qr_outf(Tensor self,
Pointer mode,
Tensor Q,
Tensor R) |
static TensorTensorTuple |
torch.linalg_qr(Tensor self) |
static TensorTensorTuple |
torch.linalg_qr(Tensor self,
Pointer mode) |
static PointerPointer<Tensor> |
torch.linalg_slogdet_out(Tensor sign,
Tensor logabsdet,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_slogdet_outf(Tensor self,
Tensor sign,
Tensor logabsdet) |
static TensorTensorTuple |
torch.linalg_slogdet(Tensor self) |
static Tensor |
torch.linalg_solve_out(Tensor out,
Tensor input,
Tensor other) |
static Tensor |
torch.linalg_solve_outf(Tensor input,
Tensor other,
Tensor out) |
static Tensor |
torch.linalg_solve(Tensor input,
Tensor other) |
static PointerPointer<Tensor> |
torch.linalg_svd_out(Tensor U,
Tensor S,
Tensor Vh,
Tensor self) |
static PointerPointer<Tensor> |
torch.linalg_svd_out(Tensor U,
Tensor S,
Tensor Vh,
Tensor self,
boolean full_matrices) |
static PointerPointer<Tensor> |
torch.linalg_svd_outf(Tensor self,
boolean full_matrices,
Tensor U,
Tensor S,
Tensor Vh) |
static TensorTensorTensorTuple |
torch.linalg_svd(Tensor self) |
static TensorTensorTensorTuple |
torch.linalg_svd(Tensor self,
boolean full_matrices) |
static Tensor |
torch.linalg_svdvals_out(Tensor out,
Tensor input) |
static Tensor |
torch.linalg_svdvals_outf(Tensor input,
Tensor out) |
static Tensor |
torch.linalg_svdvals(Tensor input) |
static Tensor |
torch.linalg_tensorinv_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_tensorinv_out(Tensor out,
Tensor self,
long ind) |
static Tensor |
torch.linalg_tensorinv_outf(Tensor self,
long ind,
Tensor out) |
static Tensor |
torch.linalg_tensorinv(Tensor self) |
static Tensor |
torch.linalg_tensorinv(Tensor self,
long ind) |
static Tensor |
torch.linalg_tensorsolve_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.linalg_tensorsolve_out(Tensor out,
Tensor self,
Tensor other,
LongArrayRefOptional dims) |
static Tensor |
torch.linalg_tensorsolve_outf(Tensor self,
Tensor other,
LongArrayRefOptional dims,
Tensor out) |
static Tensor |
torch.linalg_tensorsolve(Tensor self,
Tensor other) |
static Tensor |
torch.linalg_tensorsolve(Tensor self,
Tensor other,
LongArrayRefOptional dims) |
static Tensor |
torch.linalg_vector_norm_out(Tensor out,
Tensor self) |
static Tensor |
torch.linalg_vector_norm_out(Tensor out,
Tensor self,
Scalar ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linalg_vector_norm_outf(Tensor self,
Scalar ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.linalg_vector_norm(Tensor self) |
static Tensor |
torch.linalg_vector_norm(Tensor self,
Scalar ord,
LongArrayRefOptional dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.linear_out(Tensor out,
Tensor input,
Tensor weight) |
static Tensor |
torch.linear_out(Tensor out,
Tensor input,
Tensor weight,
TensorOptional bias) |
static Tensor |
torch.linear_outf(Tensor input,
Tensor weight,
TensorOptional bias,
Tensor out) |
static Tensor |
torch.linear(Tensor input,
Tensor weight) |
static Tensor |
torch.linear(Tensor input,
Tensor weight,
Tensor bias) |
static Tensor |
torch.linear(Tensor input,
Tensor weight,
TensorOptional bias) |
static Tensor |
torch.linspace_out(Tensor out,
Scalar start,
Scalar end) |
static Tensor |
torch.linspace_out(Tensor out,
Scalar start,
Scalar end,
LongOptional steps) |
static Tensor |
torch.linspace_outf(Scalar start,
Scalar end,
LongOptional steps,
Tensor out) |
static Tensor |
torch.local_response_norm(Tensor input,
LocalResponseNormOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.local_response_norm
/** about the exact behavior of this functional.
|
static Tensor |
torch.local_response_norm(Tensor input,
long size,
double alpha,
double beta,
double k) |
static Tensor |
torch.log_(Tensor self) |
static Tensor |
torch.log_out(Tensor out,
Tensor self) |
static Tensor |
torch.log_outf(Tensor self,
Tensor out) |
static Tensor |
torch.log_sigmoid_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor buffer) |
static Tensor |
torch.log_sigmoid_backward_outf(Tensor grad_output,
Tensor self,
Tensor buffer,
Tensor grad_input) |
static Tensor |
torch.log_sigmoid_backward(Tensor grad_output,
Tensor self,
Tensor buffer) |
static PointerPointer<Tensor> |
torch.log_sigmoid_forward_out(Tensor output,
Tensor buffer,
Tensor self) |
static PointerPointer<Tensor> |
torch.log_sigmoid_forward_outf(Tensor self,
Tensor output,
Tensor buffer) |
static TensorTensorTuple |
torch.log_sigmoid_forward(Tensor self) |
static Tensor |
torch.log_sigmoid_out(Tensor out,
Tensor self) |
static Tensor |
torch.log_sigmoid_outf(Tensor self,
Tensor out) |
static Tensor |
torch.log_sigmoid(Tensor self) |
static Tensor |
torch.log_softmax(Tensor self,
Dimname dim) |
static Tensor |
torch.log_softmax(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.log_softmax(Tensor input,
LogSoftmaxFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.log_softmax
/** about the exact behavior of this functional.
|
static Tensor |
torch.log_softmax(Tensor self,
long dim) |
static Tensor |
torch.log_softmax(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.log(Tensor self) |
static Tensor |
torch.log10_(Tensor self) |
static Tensor |
torch.log10_out(Tensor out,
Tensor self) |
static Tensor |
torch.log10_outf(Tensor self,
Tensor out) |
static Tensor |
torch.log10(Tensor self) |
static Tensor |
torch.log1p_(Tensor self) |
static Tensor |
torch.log1p_out(Tensor out,
Tensor self) |
static Tensor |
torch.log1p_outf(Tensor self,
Tensor out) |
static Tensor |
torch.log1p(Tensor self) |
static Tensor |
torch.log2_(Tensor self) |
static Tensor |
torch.log2_out(Tensor out,
Tensor self) |
static Tensor |
torch.log2_outf(Tensor self,
Tensor out) |
static Tensor |
torch.log2(Tensor self) |
static Tensor |
torch.logaddexp_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.logaddexp_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.logaddexp(Tensor self,
Tensor other) |
static Tensor |
torch.logaddexp2_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.logaddexp2_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.logaddexp2(Tensor self,
Tensor other) |
static Tensor |
torch.logcumsumexp_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.logcumsumexp_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.logcumsumexp_outf(Tensor self,
Dimname dim,
Tensor out) |
static Tensor |
torch.logcumsumexp_outf(Tensor self,
long dim,
Tensor out) |
static Tensor |
torch.logcumsumexp(Tensor self,
Dimname dim) |
static Tensor |
torch.logcumsumexp(Tensor self,
long dim) |
static Tensor |
torch.logdet(Tensor self) |
static Tensor |
torch.logical_and_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.logical_and_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.logical_and(Tensor self,
Tensor other) |
static Tensor |
torch.logical_not_out(Tensor out,
Tensor self) |
static Tensor |
torch.logical_not_outf(Tensor self,
Tensor out) |
static Tensor |
torch.logical_not(Tensor self) |
static Tensor |
torch.logical_or_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.logical_or_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.logical_or(Tensor self,
Tensor other) |
static Tensor |
torch.logical_xor_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.logical_xor_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.logical_xor(Tensor self,
Tensor other) |
static Tensor |
torch.logit_(Tensor self) |
static Tensor |
torch.logit_(Tensor self,
DoubleOptional eps) |
static Tensor |
torch.logit_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self) |
static Tensor |
torch.logit_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
DoubleOptional eps) |
static Tensor |
torch.logit_backward_outf(Tensor grad_output,
Tensor self,
DoubleOptional eps,
Tensor grad_input) |
static Tensor |
torch.logit_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.logit_backward(Tensor grad_output,
Tensor self,
DoubleOptional eps) |
static Tensor |
torch.logit_out(Tensor out,
Tensor self) |
static Tensor |
torch.logit_out(Tensor out,
Tensor self,
DoubleOptional eps) |
static Tensor |
torch.logit_outf(Tensor self,
DoubleOptional eps,
Tensor out) |
static Tensor |
torch.logit(Tensor self) |
static Tensor |
torch.logit(Tensor self,
DoubleOptional eps) |
static Tensor |
torch.logsigmoid(Tensor input) |
static Tensor |
torch.logspace_out(Tensor out,
Scalar start,
Scalar end) |
static Tensor |
torch.logspace_out(Tensor out,
Scalar start,
Scalar end,
LongOptional steps,
double base) |
static Tensor |
torch.logspace_outf(Scalar start,
Scalar end,
LongOptional steps,
double base,
Tensor out) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
boolean keepdim) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.logsumexp_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.logsumexp_outf(Tensor self,
DimnameArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.logsumexp_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.logsumexp_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.logsumexp(Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.logsumexp(Tensor self,
DimnameArrayRef dim,
boolean keepdim) |
static Tensor |
torch.logsumexp(Tensor self,
long... dim) |
static Tensor |
torch.logsumexp(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.logsumexp(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.logsumexp(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.lp_pool1d(Tensor input,
double norm_type,
LongPointer kernel_size,
LongPointer stride,
boolean ceil_mode) |
static Tensor |
torch.lp_pool1d(Tensor input,
LPPool1dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.lp_pool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.lp_pool2d(Tensor input,
double norm_type,
LongPointer kernel_size,
LongPointer stride,
boolean ceil_mode) |
static Tensor |
torch.lp_pool2d(Tensor input,
LPPool2dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.lp_pool2d
/** about the exact behavior of this functional.
|
static TensorTensorTuple |
torch.lstm_cell(Tensor input,
TensorArrayRef hx,
Tensor w_ih,
Tensor w_hh) |
static TensorTensorTuple |
torch.lstm_cell(Tensor input,
TensorArrayRef hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static TensorTensorTensorTuple |
torch.lstm(Tensor input,
TensorArrayRef hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static TensorTensorTensorTuple |
torch.lstm(Tensor data,
Tensor batch_sizes,
TensorArrayRef hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static PointerPointer<Tensor> |
torch.lstsq_out(Tensor X,
Tensor qr,
Tensor self,
Tensor A) |
static PointerPointer<Tensor> |
torch.lstsq_outf(Tensor self,
Tensor A,
Tensor X,
Tensor qr) |
static TensorTensorTuple |
torch.lstsq(Tensor self,
Tensor A) |
static Tensor |
torch.lt_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.lt_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.lt_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.lt_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.lt(Tensor self,
Scalar other) |
static Tensor |
torch.lt(Tensor self,
Tensor other) |
static Tensor |
torch.lu_solve_out(Tensor out,
Tensor self,
Tensor LU_data,
Tensor LU_pivots) |
static Tensor |
torch.lu_solve_outf(Tensor self,
Tensor LU_data,
Tensor LU_pivots,
Tensor out) |
static Tensor |
torch.lu_solve(Tensor self,
Tensor LU_data,
Tensor LU_pivots) |
static PointerPointer<Tensor> |
torch.lu_unpack_out(Tensor P,
Tensor L,
Tensor U,
Tensor LU_data,
Tensor LU_pivots) |
static PointerPointer<Tensor> |
torch.lu_unpack_out(Tensor P,
Tensor L,
Tensor U,
Tensor LU_data,
Tensor LU_pivots,
boolean unpack_data,
boolean unpack_pivots) |
static PointerPointer<Tensor> |
torch.lu_unpack_outf(Tensor LU_data,
Tensor LU_pivots,
boolean unpack_data,
boolean unpack_pivots,
Tensor P,
Tensor L,
Tensor U) |
static TensorTensorTensorTuple |
torch.lu_unpack(Tensor LU_data,
Tensor LU_pivots) |
static TensorTensorTensorTuple |
torch.lu_unpack(Tensor LU_data,
Tensor LU_pivots,
boolean unpack_data,
boolean unpack_pivots) |
static Tensor |
torch.make_variable_differentiable_view(Tensor data,
Pointer backward_info,
Pointer forward_info,
boolean shared_view_info,
byte creation_meta) |
static Tensor |
torch.make_variable_differentiable_view(Tensor data,
Pointer backward_info,
Pointer forward_info,
boolean shared_view_info,
byte creation_meta,
boolean allow_tensor_metadata_change) |
static Tensor |
torch.make_variable_differentiable_view(Tensor data,
Pointer backward_info,
Pointer forward_info,
boolean shared_view_info,
torch.CreationMeta creation_meta) |
static Tensor |
torch.make_variable_differentiable_view(Tensor data,
Pointer backward_info,
Pointer forward_info,
boolean shared_view_info,
torch.CreationMeta creation_meta,
boolean allow_tensor_metadata_change)
Creates a
Variable that is a *view* of another (*base*) variable. |
static Tensor |
torch.make_variable_non_differentiable_view(Tensor base,
Tensor data) |
static Tensor |
torch.make_variable_non_differentiable_view(Tensor base,
Tensor data,
boolean allow_tensor_metadata_change) |
static Tensor |
torch.make_variable(Tensor data) |
static Tensor |
torch.make_variable(Tensor data,
boolean requires_grad,
boolean allow_tensor_metadata_change)
Creates a
Variable from the given Tensor, copying its underlying TensorImpl. |
static Tensor |
torch.make_variable(Tensor data,
Edge gradient_edge) |
static Tensor |
torch.make_variable(Tensor data,
Edge gradient_edge,
boolean allow_tensor_metadata_change)
Creates a
Variable from the given Tensor, copying its underlying TensorImpl. |
static Tensor |
torch.margin_ranking_loss(Tensor input1,
Tensor input2,
Tensor target) |
static Tensor |
torch.margin_ranking_loss(Tensor input1,
Tensor input2,
Tensor target,
double margin,
long reduction) |
static Tensor |
torch.margin_ranking_loss(Tensor input1,
Tensor input2,
Tensor target,
double margin,
loss_reduction_t reduction) |
static Tensor |
torch.margin_ranking_loss(Tensor input1,
Tensor input2,
Tensor target,
MarginRankingLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.margin_ranking_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.masked_fill(Tensor self,
Tensor mask,
Scalar value) |
static Tensor |
torch.masked_fill(Tensor self,
Tensor mask,
Tensor value) |
static Tensor |
torch.masked_scatter(Tensor self,
Tensor mask,
Tensor source) |
static Tensor |
torch.masked_select_backward(Tensor grad,
Tensor input,
Tensor mask) |
static Tensor |
torch.masked_select_out(Tensor out,
Tensor self,
Tensor mask) |
static Tensor |
torch.masked_select_outf(Tensor self,
Tensor mask,
Tensor out) |
static Tensor |
torch.masked_select(Tensor self,
Tensor mask) |
static Tensor |
torch.matmul_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.matmul_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.matmul(Tensor self,
Tensor other) |
static Tensor |
torch.matrix_exp_backward(Tensor self,
Tensor grad) |
static Tensor |
torch.matrix_exp(Tensor self) |
static Tensor |
torch.matrix_power_out(Tensor out,
Tensor self,
long n) |
static Tensor |
torch.matrix_power_outf(Tensor self,
long n,
Tensor out) |
static Tensor |
torch.matrix_power(Tensor self,
long n) |
static Tensor |
torch.matrix_rank(Tensor self) |
static Tensor |
torch.matrix_rank(Tensor self,
boolean symmetric) |
static Tensor |
torch.matrix_rank(Tensor self,
double tol) |
static Tensor |
torch.matrix_rank(Tensor self,
double tol,
boolean symmetric) |
static Tensor |
torch.max_out(Tensor out,
Tensor self,
Tensor other) |
static PointerPointer<Tensor> |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.max_out(Tensor max,
Tensor max_values,
Tensor self,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.max_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor max,
Tensor max_values) |
static PointerPointer<Tensor> |
torch.max_outf(Tensor self,
long dim,
boolean keepdim,
Tensor max,
Tensor max_values) |
static Tensor |
torch.max_outf(Tensor self,
Tensor other,
Tensor out) |
static TensorTensorTuple |
torch.max_pool1d_with_indices(Tensor self,
long... kernel_size) |
static TensorTensorTuple |
torch.max_pool1d_with_indices(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static TensorTensorTuple |
torch.max_pool1d_with_indices(Tensor self,
LongArrayRef kernel_size) |
static TensorTensorTuple |
torch.max_pool1d_with_indices(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static TensorTensorTuple |
torch.max_pool1d_with_indices(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static TensorTensorTuple |
torch.max_pool1d_with_indices(Tensor input,
MaxPool1dOptions options)
See the documentation for
torch::nn::functional::MaxPool1dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static Tensor |
torch.max_pool1d(Tensor self,
long... kernel_size) |
static Tensor |
torch.max_pool1d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool1d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.max_pool1d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool1d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool1d(Tensor input,
MaxPool1dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_pool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max_pool2d_with_indices_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool2d_with_indices_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool2d_with_indices_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.max_pool2d_with_indices_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.max_pool2d_with_indices_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool2d_with_indices_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long... kernel_size) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_outf(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.max_pool2d_with_indices_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static TensorTensorTuple |
torch.max_pool2d_with_indices(Tensor self,
long... kernel_size) |
static TensorTensorTuple |
torch.max_pool2d_with_indices(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static TensorTensorTuple |
torch.max_pool2d_with_indices(Tensor self,
LongArrayRef kernel_size) |
static TensorTensorTuple |
torch.max_pool2d_with_indices(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static TensorTensorTuple |
torch.max_pool2d_with_indices(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static TensorTensorTuple |
torch.max_pool2d_with_indices(Tensor input,
MaxPool2dOptions options)
See the documentation for
torch::nn::functional::MaxPool2dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static Tensor |
torch.max_pool2d(Tensor self,
long... kernel_size) |
static Tensor |
torch.max_pool2d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool2d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.max_pool2d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool2d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool2d(Tensor input,
MaxPool2dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_pool2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max_pool3d_with_indices_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool3d_with_indices_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool3d_with_indices_backward_outf(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.max_pool3d_with_indices_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices,
Tensor grad_input) |
static Tensor |
torch.max_pool3d_with_indices_backward(Tensor grad_output,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor indices) |
static Tensor |
torch.max_pool3d_with_indices_backward(Tensor grad_output,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor indices) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long... kernel_size) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_out(Tensor out,
Tensor indices,
Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_outf(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static PointerPointer<Tensor> |
torch.max_pool3d_with_indices_outf(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode,
Tensor out,
Tensor indices) |
static TensorTensorTuple |
torch.max_pool3d_with_indices(Tensor self,
long... kernel_size) |
static TensorTensorTuple |
torch.max_pool3d_with_indices(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static TensorTensorTuple |
torch.max_pool3d_with_indices(Tensor self,
LongArrayRef kernel_size) |
static TensorTensorTuple |
torch.max_pool3d_with_indices(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static TensorTensorTuple |
torch.max_pool3d_with_indices(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static TensorTensorTuple |
torch.max_pool3d_with_indices(Tensor input,
MaxPool3dOptions options)
See the documentation for
torch::nn::functional::MaxPool3dFuncOptions class to learn what
/** optional arguments are supported for this functional. |
static Tensor |
torch.max_pool3d(Tensor self,
long... kernel_size) |
static Tensor |
torch.max_pool3d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool3d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.max_pool3d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool3d(Tensor input,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongPointer dilation,
boolean ceil_mode) |
static Tensor |
torch.max_pool3d(Tensor input,
MaxPool3dOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_pool3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max_unpool1d(Tensor input,
Tensor indices,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongVectorOptional output_size) |
static Tensor |
torch.max_unpool1d(Tensor input,
Tensor indices,
MaxUnpool1dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_unpool1d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max_unpool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices,
long... output_size) |
static Tensor |
torch.max_unpool2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size) |
static Tensor |
torch.max_unpool2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
long[] output_size,
Tensor grad_input) |
static Tensor |
torch.max_unpool2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size,
Tensor grad_input) |
static Tensor |
torch.max_unpool2d_backward(Tensor grad_output,
Tensor self,
Tensor indices,
long... output_size) |
static Tensor |
torch.max_unpool2d_backward(Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size) |
static Tensor |
torch.max_unpool2d_out(Tensor out,
Tensor self,
Tensor indices,
long... output_size) |
static Tensor |
torch.max_unpool2d_out(Tensor out,
Tensor self,
Tensor indices,
LongArrayRef output_size) |
static Tensor |
torch.max_unpool2d_outf(Tensor self,
Tensor indices,
long[] output_size,
Tensor out) |
static Tensor |
torch.max_unpool2d_outf(Tensor self,
Tensor indices,
LongArrayRef output_size,
Tensor out) |
static Tensor |
torch.max_unpool2d(Tensor self,
Tensor indices,
long... output_size) |
static Tensor |
torch.max_unpool2d(Tensor self,
Tensor indices,
LongArrayRef output_size) |
static Tensor |
torch.max_unpool2d(Tensor input,
Tensor indices,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongVectorOptional output_size) |
static Tensor |
torch.max_unpool2d(Tensor input,
Tensor indices,
MaxUnpool2dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_unpool2d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max_unpool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long... padding) |
static Tensor |
torch.max_unpool3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.max_unpool3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.max_unpool3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.max_unpool3d_backward(Tensor grad_output,
Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long... padding) |
static Tensor |
torch.max_unpool3d_backward(Tensor grad_output,
Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.max_unpool3d_out(Tensor out,
Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long... padding) |
static Tensor |
torch.max_unpool3d_out(Tensor out,
Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.max_unpool3d_outf(Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long[] padding,
Tensor out) |
static Tensor |
torch.max_unpool3d_outf(Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.max_unpool3d(Tensor self,
Tensor indices,
long[] output_size,
long[] stride,
long... padding) |
static Tensor |
torch.max_unpool3d(Tensor self,
Tensor indices,
LongArrayRef output_size,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.max_unpool3d(Tensor input,
Tensor indices,
LongPointer kernel_size,
LongPointer stride,
LongPointer padding,
LongVectorOptional output_size) |
static Tensor |
torch.max_unpool3d(Tensor input,
Tensor indices,
MaxUnpool3dFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.max_unpool3d
/** about the exact behavior of this functional.
|
static Tensor |
torch.max(Tensor self) |
static TensorTensorTuple |
torch.max(Tensor self,
Dimname dim) |
static TensorTensorTuple |
torch.max(Tensor self,
Dimname dim,
boolean keepdim) |
static TensorTensorTuple |
torch.max(Tensor self,
long dim) |
static TensorTensorTuple |
torch.max(Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.max(Tensor self,
Tensor other) |
static Tensor |
torch.maximum_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.maximum_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.maximum(Tensor self,
Tensor other) |
static Pointer |
torch.maybe_data_ptr(Tensor tensor) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.mean_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean_outf(Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.mean_outf(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.mean_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.mean(Tensor self) |
static Tensor |
torch.mean(Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.mean(Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean(Tensor self,
long... dim) |
static Tensor |
torch.mean(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.mean(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.mean(Tensor self,
ScalarTypeOptional dtype) |
static PointerPointer<Tensor> |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.median_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.median_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.median_outf(Tensor self,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static Tensor |
torch.median(Tensor self) |
static TensorTensorTuple |
torch.median(Tensor self,
Dimname dim) |
static TensorTensorTuple |
torch.median(Tensor self,
Dimname dim,
boolean keepdim) |
static TensorTensorTuple |
torch.median(Tensor self,
long dim) |
static TensorTensorTuple |
torch.median(Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.min_out(Tensor out,
Tensor self,
Tensor other) |
static PointerPointer<Tensor> |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.min_out(Tensor min,
Tensor min_indices,
Tensor self,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.min_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor min,
Tensor min_indices) |
static PointerPointer<Tensor> |
torch.min_outf(Tensor self,
long dim,
boolean keepdim,
Tensor min,
Tensor min_indices) |
static Tensor |
torch.min_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.min(Tensor self) |
static TensorTensorTuple |
torch.min(Tensor self,
Dimname dim) |
static TensorTensorTuple |
torch.min(Tensor self,
Dimname dim,
boolean keepdim) |
static TensorTensorTuple |
torch.min(Tensor self,
long dim) |
static TensorTensorTuple |
torch.min(Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.min(Tensor self,
Tensor other) |
static Tensor |
torch.minimum_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.minimum_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.minimum(Tensor self,
Tensor other) |
static TensorTensorTensorTuple |
torch.miopen_batch_norm_backward(Tensor input,
Tensor grad_output,
Tensor weight,
TensorOptional running_mean,
TensorOptional running_var,
TensorOptional save_mean,
TensorOptional save_var,
double epsilon) |
static TensorTensorTensorTuple |
torch.miopen_batch_norm(Tensor input,
Tensor weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean training,
double exponential_average_factor,
double epsilon) |
static Tensor |
torch.miopen_convolution_backward_bias(Tensor grad_output) |
static Tensor |
torch.miopen_convolution_backward_input(long[] self_size,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_backward_input(LongArrayRef self_size,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_backward_weight(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_backward_weight(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static TensorTensorTensorTuple |
torch.miopen_convolution_backward(Tensor self,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.miopen_convolution_backward(Tensor self,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
BoolPointer output_mask) |
static Tensor |
torch.miopen_convolution_transpose_backward_input(Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_transpose_backward_input(Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_transpose_backward_weight(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_transpose_backward_weight(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static TensorTensorTensorTuple |
torch.miopen_convolution_transpose_backward(Tensor self,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.miopen_convolution_transpose_backward(Tensor self,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
BoolPointer output_mask) |
static Tensor |
torch.miopen_convolution_transpose(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] output_padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution_transpose(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution_backward_input(long[] self_size,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution_backward_input(LongArrayRef self_size,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution_backward_weight(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution_backward_weight(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static TensorTensorTensorTuple |
torch.miopen_depthwise_convolution_backward(Tensor self,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.miopen_depthwise_convolution_backward(Tensor self,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic,
BoolPointer output_mask) |
static Tensor |
torch.miopen_depthwise_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static Tensor |
torch.miopen_depthwise_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean benchmark,
boolean deterministic) |
static TensorTensorTensorTensorVectorTuple |
torch.miopen_rnn_backward(Tensor input,
TensorArrayRef weight,
long weight_stride0,
Tensor weight_buf,
Tensor hx,
TensorOptional cx,
Tensor output,
TensorOptional grad_output,
TensorOptional grad_hy,
TensorOptional grad_cy,
long mode,
long hidden_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
long[] batch_sizes,
TensorOptional dropout_state,
Tensor reserve,
BoolPointer output_mask) |
static TensorTensorTensorTensorVectorTuple |
torch.miopen_rnn_backward(Tensor input,
TensorArrayRef weight,
long weight_stride0,
Tensor weight_buf,
Tensor hx,
TensorOptional cx,
Tensor output,
TensorOptional grad_output,
TensorOptional grad_hy,
TensorOptional grad_cy,
long mode,
long hidden_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
LongArrayRef batch_sizes,
TensorOptional dropout_state,
Tensor reserve,
BoolPointer output_mask) |
static TensorTensorTensorTensorTensorTuple |
torch.miopen_rnn(Tensor input,
TensorArrayRef weight,
long weight_stride0,
Tensor hx,
TensorOptional cx,
long mode,
long hidden_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
long[] batch_sizes,
TensorOptional dropout_state) |
static TensorTensorTensorTensorTensorTuple |
torch.miopen_rnn(Tensor input,
TensorArrayRef weight,
long weight_stride0,
Tensor hx,
TensorOptional cx,
long mode,
long hidden_size,
long num_layers,
boolean batch_first,
double dropout,
boolean train,
boolean bidirectional,
LongArrayRef batch_sizes,
TensorOptional dropout_state) |
static Tensor |
torch.mish_(Tensor self) |
static Tensor |
torch.mish_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.mish_out(Tensor out,
Tensor self) |
static Tensor |
torch.mish_outf(Tensor self,
Tensor out) |
static Tensor |
torch.mish(Tensor self) |
static Tensor |
torch.mkldnn_adaptive_avg_pool2d_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.mkldnn_adaptive_avg_pool2d(Tensor self,
long... output_size) |
static Tensor |
torch.mkldnn_adaptive_avg_pool2d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.mkldnn_convolution_backward_input(long[] self_size,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean bias_defined) |
static Tensor |
torch.mkldnn_convolution_backward_input(LongArrayRef self_size,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean bias_defined) |
static TensorTensorTuple |
torch.mkldnn_convolution_backward_weights(long[] weight_size,
Tensor grad_output,
Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups,
boolean bias_defined) |
static TensorTensorTuple |
torch.mkldnn_convolution_backward_weights(LongArrayRef weight_size,
Tensor grad_output,
Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
boolean bias_defined) |
static TensorTensorTensorTuple |
torch.mkldnn_convolution_backward(Tensor self,
Tensor grad_output,
Tensor weight,
long[] padding,
long[] stride,
long[] dilation,
long groups,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.mkldnn_convolution_backward(Tensor self,
Tensor grad_output,
Tensor weight,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups,
BoolPointer output_mask) |
static Tensor |
torch.mkldnn_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
long[] padding,
long[] stride,
long[] dilation,
long groups) |
static Tensor |
torch.mkldnn_convolution(Tensor self,
Tensor weight,
TensorOptional bias,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.mkldnn_linear_backward_input(long[] input_size,
Tensor grad_output,
Tensor weight) |
static Tensor |
torch.mkldnn_linear_backward_input(LongArrayRef input_size,
Tensor grad_output,
Tensor weight) |
static TensorTensorTuple |
torch.mkldnn_linear_backward_weights(Tensor grad_output,
Tensor input,
Tensor weight,
boolean bias_defined) |
static TensorTensorTensorTuple |
torch.mkldnn_linear_backward(Tensor self,
Tensor grad_output,
Tensor weight,
BoolPointer output_mask) |
static Tensor |
torch.mkldnn_linear(Tensor self,
Tensor weight) |
static Tensor |
torch.mkldnn_linear(Tensor self,
Tensor weight,
TensorOptional bias) |
static Tensor |
torch.mkldnn_max_pool2d_backward(Tensor grad_output,
Tensor output,
Tensor input,
long... kernel_size) |
static Tensor |
torch.mkldnn_max_pool2d_backward(Tensor grad_output,
Tensor output,
Tensor input,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool2d_backward(Tensor grad_output,
Tensor output,
Tensor input,
LongArrayRef kernel_size) |
static Tensor |
torch.mkldnn_max_pool2d_backward(Tensor grad_output,
Tensor output,
Tensor input,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool2d(Tensor self,
long... kernel_size) |
static Tensor |
torch.mkldnn_max_pool2d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool2d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.mkldnn_max_pool2d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool3d_backward(Tensor grad_output,
Tensor output,
Tensor input,
long... kernel_size) |
static Tensor |
torch.mkldnn_max_pool3d_backward(Tensor grad_output,
Tensor output,
Tensor input,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool3d_backward(Tensor grad_output,
Tensor output,
Tensor input,
LongArrayRef kernel_size) |
static Tensor |
torch.mkldnn_max_pool3d_backward(Tensor grad_output,
Tensor output,
Tensor input,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool3d(Tensor self,
long... kernel_size) |
static Tensor |
torch.mkldnn_max_pool3d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_max_pool3d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.mkldnn_max_pool3d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.mkldnn_reorder_conv2d_weight(Tensor self) |
static Tensor |
torch.mkldnn_reorder_conv2d_weight(Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups) |
static Tensor |
torch.mkldnn_reorder_conv2d_weight(Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.mkldnn_reorder_conv3d_weight(Tensor self) |
static Tensor |
torch.mkldnn_reorder_conv3d_weight(Tensor self,
long[] padding,
long[] stride,
long[] dilation,
long groups) |
static Tensor |
torch.mkldnn_reorder_conv3d_weight(Tensor self,
LongArrayRef padding,
LongArrayRef stride,
LongArrayRef dilation,
long groups) |
static Tensor |
torch.mm_out(Tensor out,
Tensor self,
Tensor mat2) |
static Tensor |
torch.mm_outf(Tensor self,
Tensor mat2,
Tensor out) |
static Tensor |
torch.mm(Tensor self,
Tensor mat2) |
static Tensor |
torch.mod(Scalar x,
Tensor y) |
static Tensor |
torch.mod(Tensor x,
Scalar y) |
static Tensor |
torch.mod(Tensor x,
Tensor y) |
static PointerPointer<Tensor> |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self) |
static PointerPointer<Tensor> |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.mode_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.mode_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.mode_outf(Tensor self,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static TensorTensorTuple |
torch.mode(Tensor self) |
static TensorTensorTuple |
torch.mode(Tensor self,
Dimname dim) |
static TensorTensorTuple |
torch.mode(Tensor self,
Dimname dim,
boolean keepdim) |
static TensorTensorTuple |
torch.mode(Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.moveaxis(Tensor self,
long[] source,
long... destination) |
static Tensor |
torch.moveaxis(Tensor self,
LongArrayRef source,
LongArrayRef destination) |
static Tensor |
torch.moveaxis(Tensor self,
long source,
long destination) |
static Tensor |
torch.movedim(Tensor self,
long[] source,
long... destination) |
static Tensor |
torch.movedim(Tensor self,
LongArrayRef source,
LongArrayRef destination) |
static Tensor |
torch.movedim(Tensor self,
long source,
long destination) |
static Tensor |
torch.mse_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.mse_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor grad_input) |
static Tensor |
torch.mse_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.mse_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.mse_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.mse_loss_outf(Tensor self,
Tensor target,
long reduction,
Tensor out) |
static Tensor |
torch.mse_loss(Tensor self,
Tensor target) |
static Tensor |
torch.mse_loss(Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.mse_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.mse_loss(Tensor input,
Tensor target,
MSELossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.mse_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.msort_out(Tensor out,
Tensor self) |
static Tensor |
torch.msort_outf(Tensor self,
Tensor out) |
static Tensor |
torch.msort(Tensor self) |
static Tensor |
torch.mul_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.mul_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.mul(Tensor self,
Scalar other) |
static Tensor |
torch.mul(Tensor self,
Tensor other) |
static TensorTensorTuple |
torch.multi_head_attention_forward(Tensor query,
Tensor key,
Tensor value,
long embed_dim_to_check,
long num_heads,
Tensor in_proj_weight,
Tensor in_proj_bias,
Tensor bias_k,
Tensor bias_v,
boolean add_zero_attn,
double dropout_p,
Tensor out_proj_weight,
Tensor out_proj_bias) |
static TensorTensorTuple |
torch.multi_head_attention_forward(Tensor query,
Tensor key,
Tensor value,
long embed_dim_to_check,
long num_heads,
Tensor in_proj_weight,
Tensor in_proj_bias,
Tensor bias_k,
Tensor bias_v,
boolean add_zero_attn,
double dropout_p,
Tensor out_proj_weight,
Tensor out_proj_bias,
boolean training,
Tensor key_padding_mask,
boolean need_weights,
Tensor attn_mask,
boolean use_separate_proj_weight,
Tensor q_proj_weight,
Tensor k_proj_weight,
Tensor v_proj_weight,
Tensor static_k,
Tensor static_v) |
static TensorTensorTuple |
torch.multi_head_attention_forward(Tensor query,
Tensor key,
Tensor value,
MultiheadAttentionForwardFuncOptions options) |
static Tensor |
torch.multi_margin_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
Scalar p,
Scalar margin) |
static Tensor |
torch.multi_margin_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction) |
static Tensor |
torch.multi_margin_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction,
Tensor grad_input) |
static Tensor |
torch.multi_margin_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
Scalar p,
Scalar margin) |
static Tensor |
torch.multi_margin_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction) |
static Tensor |
torch.multi_margin_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.multi_margin_loss_out(Tensor out,
Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction) |
static Tensor |
torch.multi_margin_loss_outf(Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction,
Tensor out) |
static Tensor |
torch.multi_margin_loss(Tensor self,
Tensor target) |
static Tensor |
torch.multi_margin_loss(Tensor input,
Tensor target,
long p,
double margin,
Tensor weight,
loss_reduction_t reduction) |
static Tensor |
torch.multi_margin_loss(Tensor input,
Tensor target,
MultiMarginLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.multi_margin_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.multi_margin_loss(Tensor self,
Tensor target,
Scalar p,
Scalar margin,
TensorOptional weight,
long reduction) |
static Tensor |
torch.multilabel_margin_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor is_target) |
static Tensor |
torch.multilabel_margin_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor is_target,
Tensor grad_input) |
static Tensor |
torch.multilabel_margin_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor is_target) |
static PointerPointer<Tensor> |
torch.multilabel_margin_loss_forward_out(Tensor output,
Tensor is_target,
Tensor self,
Tensor target,
long reduction) |
static PointerPointer<Tensor> |
torch.multilabel_margin_loss_forward_outf(Tensor self,
Tensor target,
long reduction,
Tensor output,
Tensor is_target) |
static TensorTensorTuple |
torch.multilabel_margin_loss_forward(Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.multilabel_margin_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.multilabel_margin_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.multilabel_margin_loss_outf(Tensor self,
Tensor target,
long reduction,
Tensor out) |
static Tensor |
torch.multilabel_margin_loss(Tensor self,
Tensor target) |
static Tensor |
torch.multilabel_margin_loss(Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.multilabel_margin_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.multilabel_margin_loss(Tensor input,
Tensor target,
MultiLabelMarginLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.multilabel_margin_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.multilabel_soft_margin_loss(Tensor input,
Tensor target) |
static Tensor |
torch.multilabel_soft_margin_loss(Tensor input,
Tensor target,
MultiLabelSoftMarginLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.multilabel_soft_margin_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.multilabel_soft_margin_loss(Tensor input,
Tensor target,
Tensor weight,
loss_reduction_t reduction) |
static Tensor |
torch.multinomial_out(Tensor out,
Tensor self,
long num_samples) |
static Tensor |
torch.multinomial_out(Tensor out,
Tensor self,
long num_samples,
boolean replacement,
GeneratorOptional generator) |
static Tensor |
torch.multinomial_outf(Tensor self,
long num_samples,
boolean replacement,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.multinomial(Tensor self,
long num_samples) |
static Tensor |
torch.multinomial(Tensor self,
long num_samples,
boolean replacement,
GeneratorOptional generator) |
static Tensor |
torch.multiply_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.multiply_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.multiply(Scalar x,
Tensor y) |
static Tensor |
torch.multiply(Tensor self,
Scalar other) |
static Tensor |
torch.multiply(Tensor self,
Tensor other) |
static Tensor |
torch.mv_out(Tensor out,
Tensor self,
Tensor vec) |
static Tensor |
torch.mv_outf(Tensor self,
Tensor vec,
Tensor out) |
static Tensor |
torch.mv(Tensor self,
Tensor vec) |
static Tensor |
torch.mvlgamma_out(Tensor out,
Tensor self,
long p) |
static Tensor |
torch.mvlgamma_outf(Tensor self,
long p,
Tensor out) |
static Tensor |
torch.mvlgamma(Tensor self,
long p) |
static Tensor |
torch.nan_to_num_(Tensor self) |
static Tensor |
torch.nan_to_num_(Tensor self,
DoubleOptional nan,
DoubleOptional posinf,
DoubleOptional neginf) |
static Tensor |
torch.nan_to_num_out(Tensor out,
Tensor self) |
static Tensor |
torch.nan_to_num_out(Tensor out,
Tensor self,
DoubleOptional nan,
DoubleOptional posinf,
DoubleOptional neginf) |
static Tensor |
torch.nan_to_num_outf(Tensor self,
DoubleOptional nan,
DoubleOptional posinf,
DoubleOptional neginf,
Tensor out) |
static Tensor |
torch.nan_to_num(Tensor self) |
static Tensor |
torch.nan_to_num(Tensor self,
DoubleOptional nan,
DoubleOptional posinf,
DoubleOptional neginf) |
static Tensor |
torch.nanmean_out(Tensor out,
Tensor self) |
static Tensor |
torch.nanmean_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nanmean_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nanmean_outf(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.nanmean_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.nanmean(Tensor self) |
static Tensor |
torch.nanmean(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nanmean(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static PointerPointer<Tensor> |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
long dim) |
static PointerPointer<Tensor> |
torch.nanmedian_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean keepdim) |
static PointerPointer<Tensor> |
torch.nanmedian_outf(Tensor self,
Dimname dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.nanmedian_outf(Tensor self,
long dim,
boolean keepdim,
Tensor values,
Tensor indices) |
static Tensor |
torch.nanmedian(Tensor self) |
static TensorTensorTuple |
torch.nanmedian(Tensor self,
Dimname dim) |
static TensorTensorTuple |
torch.nanmedian(Tensor self,
Dimname dim,
boolean keepdim) |
static TensorTensorTuple |
torch.nanmedian(Tensor self,
long dim) |
static TensorTensorTuple |
torch.nanmedian(Tensor self,
long dim,
boolean keepdim) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
double q) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
double q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
Tensor q) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.nanquantile_out(Tensor out,
Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.nanquantile_outf(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation,
Tensor out) |
static Tensor |
torch.nanquantile_outf(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.nanquantile_outf(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation,
Tensor out) |
static Tensor |
torch.nanquantile_outf(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.nanquantile(Tensor self,
double q) |
static Tensor |
torch.nanquantile(Tensor self,
double q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.nanquantile(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.nanquantile(Tensor self,
Tensor q) |
static Tensor |
torch.nanquantile(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.nanquantile(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.nansum_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.nansum_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nansum_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.nansum_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nansum_outf(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.nansum_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.nansum(Tensor self) |
static Tensor |
torch.nansum(Tensor self,
long... dim) |
static Tensor |
torch.nansum(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nansum(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.nansum(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.nansum(Tensor self,
ScalarTypeOptional dtype) |
static Tensor |
torch.narrow_copy_out(Tensor out,
Tensor self,
long dim,
long start,
long length) |
static Tensor |
torch.narrow_copy_outf(Tensor self,
long dim,
long start,
long length,
Tensor out) |
static Tensor |
torch.narrow_copy(Tensor self,
long dim,
long start,
long length) |
static Tensor |
torch.narrow(Tensor self,
long dim,
long start,
long length) |
static Tensor |
torch.narrow(Tensor self,
long dim,
Tensor start,
long length) |
static TensorTensorTensorTuple |
torch.native_batch_norm_backward(Tensor grad_out,
Tensor input,
TensorOptional weight,
TensorOptional running_mean,
TensorOptional running_var,
TensorOptional save_mean,
TensorOptional save_invstd,
boolean train,
double eps,
BoolPointer output_mask) |
static PointerPointer<Tensor> |
torch.native_batch_norm_out(Tensor out,
Tensor save_mean,
Tensor save_invstd,
Tensor input,
TensorOptional weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean training,
double momentum,
double eps) |
static PointerPointer<Tensor> |
torch.native_batch_norm_outf(Tensor input,
TensorOptional weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean training,
double momentum,
double eps,
Tensor out,
Tensor save_mean,
Tensor save_invstd) |
static TensorTensorTensorTuple |
torch.native_batch_norm(Tensor input,
TensorOptional weight,
TensorOptional bias,
TensorOptional running_mean,
TensorOptional running_var,
boolean training,
double momentum,
double eps) |
static TensorTensorTensorTuple |
torch.native_group_norm_backward(Tensor grad_out,
Tensor input,
Tensor mean,
Tensor rstd,
TensorOptional weight,
long N,
long C,
long HxW,
long group,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.native_group_norm(Tensor input,
TensorOptional weight,
TensorOptional bias,
long N,
long C,
long HxW,
long group,
double eps) |
static TensorTensorTensorTuple |
torch.native_layer_norm_backward(Tensor grad_out,
Tensor input,
long[] normalized_shape,
Tensor mean,
Tensor rstd,
TensorOptional weight,
TensorOptional bias,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.native_layer_norm_backward(Tensor grad_out,
Tensor input,
LongArrayRef normalized_shape,
Tensor mean,
Tensor rstd,
TensorOptional weight,
TensorOptional bias,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.native_layer_norm(Tensor input,
long[] normalized_shape,
TensorOptional weight,
TensorOptional bias,
double eps) |
static TensorTensorTensorTuple |
torch.native_layer_norm(Tensor input,
LongArrayRef normalized_shape,
TensorOptional weight,
TensorOptional bias,
double eps) |
static Tensor |
torch.native_norm(Tensor self) |
static Tensor |
torch.native_norm(Tensor self,
Scalar p) |
static Tensor |
torch.native_norm(Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.native_norm(Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.ne_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.ne_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.ne_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.ne_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.ne(Tensor self,
Scalar other) |
static Tensor |
torch.ne(Tensor self,
Tensor other) |
static Tensor |
torch.neg_(Tensor self) |
static Tensor |
torch.neg_out(Tensor out,
Tensor self) |
static Tensor |
torch.neg_outf(Tensor self,
Tensor out) |
static Tensor |
torch.neg(Tensor self) |
static Tensor |
torch.negative_(Tensor self) |
static Tensor |
torch.negative_out(Tensor out,
Tensor self) |
static Tensor |
torch.negative_outf(Tensor self,
Tensor out) |
static Tensor |
torch.negative(Tensor self) |
static Tensor |
torch.nextafter_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.nextafter_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.nextafter(Tensor self,
Tensor other) |
static Tensor |
torch.nll_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight) |
static Tensor |
torch.nll_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight,
Tensor grad_input) |
static Tensor |
torch.nll_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight) |
static PointerPointer<Tensor> |
torch.nll_loss_forward_out(Tensor output,
Tensor total_weight,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static PointerPointer<Tensor> |
torch.nll_loss_forward_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor output,
Tensor total_weight) |
static TensorTensorTuple |
torch.nll_loss_forward(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nll_loss_nd(Tensor self,
Tensor target) |
static Tensor |
torch.nll_loss_nd(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nll_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.nll_loss_out(Tensor out,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nll_loss_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor out) |
static Tensor |
torch.nll_loss(Tensor self,
Tensor target) |
static Tensor |
torch.nll_loss(Tensor input,
Tensor target,
NLLLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.nll_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.nll_loss(Tensor input,
Tensor target,
Tensor weight,
long ignore_index,
loss_reduction_t reduction) |
static Tensor |
torch.nll_loss(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nll_loss2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight) |
static Tensor |
torch.nll_loss2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight,
Tensor grad_input) |
static Tensor |
torch.nll_loss2d_backward(Tensor grad_output,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor total_weight) |
static PointerPointer<Tensor> |
torch.nll_loss2d_forward_out(Tensor output,
Tensor total_weight,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static PointerPointer<Tensor> |
torch.nll_loss2d_forward_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor output,
Tensor total_weight) |
static TensorTensorTuple |
torch.nll_loss2d_forward(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nll_loss2d_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.nll_loss2d_out(Tensor out,
Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static Tensor |
torch.nll_loss2d_outf(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index,
Tensor out) |
static Tensor |
torch.nll_loss2d(Tensor self,
Tensor target) |
static Tensor |
torch.nll_loss2d(Tensor self,
Tensor target,
TensorOptional weight,
long reduction,
long ignore_index) |
static TensorVector |
torch.nonzero_numpy(Tensor self) |
static Tensor |
torch.nonzero_out(Tensor out,
Tensor self) |
static Tensor |
torch.nonzero_outf(Tensor self,
Tensor out) |
static Tensor |
torch.nonzero(Tensor self) |
static Tensor |
torch.norm_except_dim(Tensor v) |
static Tensor |
torch.norm_except_dim(Tensor v,
long pow,
long dim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
DimnameArrayRef dim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
long... dim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
LongArrayRef dim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.norm_out(Tensor out,
Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim,
torch.ScalarType dtype,
Tensor out) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim,
torch.ScalarType dtype,
Tensor out) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.norm_outf(Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
torch.ScalarType dtype,
Tensor out) |
static Tensor |
torch.norm(Tensor self) |
static Tensor |
torch.norm(Tensor self,
Scalar p) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
DimnameArrayRef dim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
DimnameArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
long... dim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
long[] dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
LongArrayRef dim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
LongArrayRef dim,
boolean keepdim,
torch.ScalarType dtype) |
static Tensor |
torch.norm(Tensor self,
ScalarOptional p,
torch.ScalarType dtype) |
static Tensor |
torch.normal_(Tensor tensor) |
static Tensor |
torch.normal_(Tensor tensor,
double mean,
double std)
Fills the given 2-dimensional
matrix with values drawn from a normal
distribution parameterized by mean and std. |
static Tensor |
torch.normal_out(Tensor out,
double mean,
double std,
long... size) |
static Tensor |
torch.normal_out(Tensor out,
double mean,
double std,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.normal_out(Tensor out,
double mean,
double std,
LongArrayRef size) |
static Tensor |
torch.normal_out(Tensor out,
double mean,
double std,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.normal_out(Tensor out,
double mean,
Tensor std) |
static Tensor |
torch.normal_out(Tensor out,
double mean,
Tensor std,
GeneratorOptional generator) |
static Tensor |
torch.normal_out(Tensor out,
Tensor mean) |
static Tensor |
torch.normal_out(Tensor out,
Tensor mean,
double std,
GeneratorOptional generator) |
static Tensor |
torch.normal_out(Tensor out,
Tensor mean,
Tensor std) |
static Tensor |
torch.normal_out(Tensor out,
Tensor mean,
Tensor std,
GeneratorOptional generator) |
static Tensor |
torch.normal_outf(double mean,
double std,
long[] size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.normal_outf(double mean,
double std,
LongArrayRef size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.normal_outf(double mean,
Tensor std,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.normal_outf(Tensor mean,
double std,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.normal_outf(Tensor mean,
Tensor std,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.normal(double mean,
Tensor std) |
static Tensor |
torch.normal(double mean,
Tensor std,
GeneratorOptional generator) |
static Tensor |
torch.normal(Tensor mean) |
static Tensor |
torch.normal(Tensor mean,
double std,
GeneratorOptional generator) |
static Tensor |
torch.normal(Tensor mean,
Tensor std) |
static Tensor |
torch.normal(Tensor mean,
Tensor std,
GeneratorOptional generator) |
static Tensor |
torch.normalize(Tensor input) |
static Tensor |
torch.normalize(Tensor input,
double p,
long dim,
double eps,
TensorOptional out) |
static Tensor |
torch.normalize(Tensor input,
NormalizeFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.normalize
/** about the exact behavior of this functional.
|
static Tensor |
torch.not_equal_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.not_equal_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.not_equal_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.not_equal_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.not_equal(Tensor self,
Scalar other) |
static Tensor |
torch.not_equal(Tensor self,
Tensor other) |
static Tensor |
torch.notEquals(Scalar x,
Tensor y) |
static Tensor |
torch.notEquals(Tensor x,
Scalar y) |
static Tensor |
torch.notEquals(Tensor x,
Tensor y) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self,
boolean keepdim) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.nuclear_norm_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.nuclear_norm_outf(Tensor self,
boolean keepdim,
Tensor out) |
static Tensor |
torch.nuclear_norm_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.nuclear_norm_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.nuclear_norm(Tensor self) |
static Tensor |
torch.nuclear_norm(Tensor self,
boolean keepdim) |
static Tensor |
torch.nuclear_norm(Tensor self,
long... dim) |
static Tensor |
torch.nuclear_norm(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.nuclear_norm(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.nuclear_norm(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static long |
torch.numel(Tensor tensor) |
static Tensor |
torch.one_hot(Tensor self) |
static Tensor |
torch.one_hot(Tensor self,
long num_classes) |
static Tensor |
torch.ones_(Tensor tensor)
Fills the given
tensor with ones. |
static Tensor |
torch.ones_like(Tensor self) |
static Tensor |
torch.ones_like(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.ones_like(Tensor self,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.ones_out(Tensor out,
long... size) |
static Tensor |
torch.ones_out(Tensor out,
LongArrayRef size) |
static Tensor |
torch.ones_outf(long[] size,
Tensor out) |
static Tensor |
torch.ones_outf(LongArrayRef size,
Tensor out) |
static Tensor |
torch.or(Scalar x,
Tensor y) |
static Tensor |
torch.or(Tensor x,
Scalar y) |
static Tensor |
torch.or(Tensor x,
Tensor y) |
static Tensor |
torch.orgqr_out(Tensor out,
Tensor self,
Tensor input2) |
static Tensor |
torch.orgqr_outf(Tensor self,
Tensor input2,
Tensor out) |
static Tensor |
torch.orgqr(Tensor self,
Tensor input2) |
static Tensor |
torch.ormqr_out(Tensor out,
Tensor self,
Tensor input2,
Tensor input3) |
static Tensor |
torch.ormqr_out(Tensor out,
Tensor self,
Tensor input2,
Tensor input3,
boolean left,
boolean transpose) |
static Tensor |
torch.ormqr_outf(Tensor self,
Tensor input2,
Tensor input3,
boolean left,
boolean transpose,
Tensor out) |
static Tensor |
torch.ormqr(Tensor self,
Tensor input2,
Tensor input3) |
static Tensor |
torch.ormqr(Tensor self,
Tensor input2,
Tensor input3,
boolean left,
boolean transpose) |
static Tensor |
torch.orthogonal_(Tensor tensor) |
static Tensor |
torch.orthogonal_(Tensor tensor,
double gain)
Fills the input
Tensor with a (semi) orthogonal matrix, as described in
"Exact solutions to the nonlinear dynamics of learning in deep linear neural
networks" - Saxe, A. |
static Tensor |
torch.outer_out(Tensor out,
Tensor self,
Tensor vec2) |
static Tensor |
torch.outer_outf(Tensor self,
Tensor vec2,
Tensor out) |
static Tensor |
torch.outer(Tensor self,
Tensor vec2) |
static PackedSequence |
torch.pack_padded_sequence(Tensor input,
Tensor lengths) |
static PackedSequence |
torch.pack_padded_sequence(Tensor input,
Tensor lengths,
boolean batch_first,
boolean enforce_sorted)
Packs a Tensor containing padded sequences of variable length.
|
static Tensor |
torch.pad(Tensor input,
long[] pad,
pad_mode_t mode,
double value) |
static Tensor |
torch.pad(Tensor input,
LongArrayRef pad,
pad_mode_t mode,
double value) |
static Tensor |
torch.pad(Tensor input,
PadFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.pad
/** about the exact behavior of this functional.
|
static Tensor |
torch.pairwise_distance(Tensor x1,
Tensor x2) |
static Tensor |
torch.pairwise_distance(Tensor x1,
Tensor x2,
double p,
double eps,
boolean keepdim) |
static Tensor |
torch.pairwise_distance(Tensor x1,
Tensor x2,
PairwiseDistanceOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.pairwise_distance
/** about the exact behavior of this functional.
|
static Tensor |
torch.parameters_to_vector(Tensor parameters) |
static Tensor |
torch.pdist(Tensor self) |
static Tensor |
torch.pdist(Tensor self,
double p) |
static Tensor |
torch.permute(Tensor self,
long... dims) |
static Tensor |
torch.permute(Tensor self,
LongArrayRef dims) |
static Tensor |
torch.pinverse(Tensor self) |
static Tensor |
torch.pinverse(Tensor self,
double rcond) |
static Tensor |
torch.pixel_shuffle(Tensor self,
long upscale_factor) |
static Tensor |
torch.pixel_shuffle(Tensor input,
PixelShuffleOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.pixel_shuffle
/** about the exact behavior of this functional.
|
static Tensor |
torch.pixel_unshuffle(Tensor self,
long downscale_factor) |
static Tensor |
torch.pixel_unshuffle(Tensor input,
PixelUnshuffleOptions options) |
static Tensor |
torch.poisson_nll_loss(Tensor input,
Tensor target) |
static Tensor |
torch.poisson_nll_loss(Tensor input,
Tensor target,
boolean log_input,
boolean full,
double eps,
long reduction) |
static Tensor |
torch.poisson_nll_loss(Tensor input,
Tensor target,
boolean log_input,
boolean full,
double eps,
loss_reduction_t reduction) |
static Tensor |
torch.poisson_nll_loss(Tensor input,
Tensor target,
PoissonNLLLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.poisson_nll_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.poisson(Tensor self) |
static Tensor |
torch.poisson(Tensor self,
GeneratorOptional generator) |
static Tensor |
torch.polar_out(Tensor out,
Tensor abs,
Tensor angle) |
static Tensor |
torch.polar_outf(Tensor abs,
Tensor angle,
Tensor out) |
static Tensor |
torch.polar(Tensor abs,
Tensor angle) |
static Tensor |
torch.polygamma_out(Tensor out,
long n,
Tensor self) |
static Tensor |
torch.polygamma_outf(long n,
Tensor self,
Tensor out) |
static Tensor |
torch.polygamma(long n,
Tensor self) |
static Tensor |
torch.positive(Tensor self) |
static Tensor |
torch.pow_out(Tensor out,
Scalar self,
Tensor exponent) |
static Tensor |
torch.pow_out(Tensor out,
Tensor self,
Scalar exponent) |
static Tensor |
torch.pow_out(Tensor out,
Tensor self,
Tensor exponent) |
static Tensor |
torch.pow_outf(Scalar self,
Tensor exponent,
Tensor out) |
static Tensor |
torch.pow_outf(Tensor self,
Scalar exponent,
Tensor out) |
static Tensor |
torch.pow_outf(Tensor self,
Tensor exponent,
Tensor out) |
static Tensor |
torch.pow(Scalar self,
Tensor exponent) |
static Tensor |
torch.pow(Tensor self,
Scalar exponent) |
static Tensor |
torch.pow(Tensor self,
Tensor exponent) |
static TensorTensorTuple |
torch.prelu_backward(Tensor grad_output,
Tensor self,
Tensor weight) |
static Tensor |
torch.prelu(Tensor self,
Tensor weight) |
static Pointer |
torch.print(Pointer stream,
Tensor tensor,
long linesize) |
static void |
torch.print(Tensor t) |
static void |
torch.print(Tensor t,
long linesize) |
static Tensor |
torch.prod_out(Tensor out,
Tensor self,
Dimname dim) |
static Tensor |
torch.prod_out(Tensor out,
Tensor self,
Dimname dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.prod_out(Tensor out,
Tensor self,
long dim) |
static Tensor |
torch.prod_out(Tensor out,
Tensor self,
long dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.prod_outf(Tensor self,
Dimname dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.prod_outf(Tensor self,
long dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.prod(Tensor self) |
static Tensor |
torch.prod(Tensor self,
Dimname dim) |
static Tensor |
torch.prod(Tensor self,
Dimname dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.prod(Tensor self,
long dim) |
static Tensor |
torch.prod(Tensor self,
long dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.prod(Tensor self,
ScalarTypeOptional dtype) |
static void |
torch.propagate_names_except(Tensor result,
Tensor src,
long... excluded_idxs) |
static void |
torch.propagate_names_except(Tensor result,
Tensor src,
LongArrayRef excluded_idxs) |
static DimnameVector |
torch.propagate_names_for_addmm(Tensor m1,
Tensor m2,
Tensor bias) |
static DimnameVector |
torch.propagate_names_for_addmv(Tensor mat,
Tensor vec,
Tensor bias) |
static void |
torch.propagate_names_for_expand(Tensor result,
Tensor self) |
static void |
torch.propagate_names_for_reduction(Tensor result,
Tensor src,
long[] excluded_idxs,
boolean keepdim) |
static void |
torch.propagate_names_for_reduction(Tensor result,
Tensor src,
LongArrayRef excluded_idxs,
boolean keepdim) |
static Tensor |
torch.propagate_names_if_nonempty(Tensor result,
DimnameArrayRef maybe_names) |
static Tensor |
torch.propagate_names_if_nonempty(Tensor result,
DimnameArrayRef maybe_names,
boolean validate_names) |
static Tensor |
torch.propagate_names(Tensor result,
DimnameArrayRef names) |
static Tensor |
torch.propagate_names(Tensor result,
DimnameArrayRef names,
boolean validate_names) |
static void |
torch.propagate_names(Tensor result,
Tensor src) |
static Tensor |
torch.put(Tensor self,
Tensor index,
Tensor source) |
static Tensor |
torch.put(Tensor self,
Tensor index,
Tensor source,
boolean accumulate) |
static long |
torch.q_per_channel_axis(Tensor self) |
static Tensor |
torch.q_per_channel_scales(Tensor self) |
static Tensor |
torch.q_per_channel_zero_points(Tensor self) |
static double |
torch.q_scale(Tensor self) |
static long |
torch.q_zero_point(Tensor self) |
static PointerPointer<Tensor> |
torch.qr_out(Tensor Q,
Tensor R,
Tensor self) |
static PointerPointer<Tensor> |
torch.qr_out(Tensor Q,
Tensor R,
Tensor self,
boolean some) |
static PointerPointer<Tensor> |
torch.qr_outf(Tensor self,
boolean some,
Tensor Q,
Tensor R) |
static TensorTensorTuple |
torch.qr(Tensor self) |
static TensorTensorTuple |
torch.qr(Tensor self,
boolean some) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
double q) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
double q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
Tensor q) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.quantile_out(Tensor out,
Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.quantile_outf(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation,
Tensor out) |
static Tensor |
torch.quantile_outf(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.quantile_outf(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation,
Tensor out) |
static Tensor |
torch.quantile_outf(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.quantile(Tensor self,
double q) |
static Tensor |
torch.quantile(Tensor self,
double q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.quantile(Tensor self,
double q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.quantile(Tensor self,
Tensor q) |
static Tensor |
torch.quantile(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim) |
static Tensor |
torch.quantile(Tensor self,
Tensor q,
LongOptional dim,
boolean keepdim,
Pointer interpolation) |
static Tensor |
torch.quantize_per_channel(Tensor self,
Tensor scales,
Tensor zero_points,
long axis,
torch.ScalarType dtype) |
static TensorVector |
torch.quantize_per_tensor(TensorArrayRef tensors,
Tensor scales,
Tensor zero_points,
torch.ScalarType dtype) |
static Tensor |
torch.quantize_per_tensor(Tensor self,
double scale,
long zero_point,
torch.ScalarType dtype) |
static Tensor |
torch.quantize_per_tensor(Tensor self,
Tensor scale,
Tensor zero_point,
torch.ScalarType dtype) |
static Tensor |
torch.quantized_batch_norm(Tensor input,
TensorOptional weight,
TensorOptional bias,
Tensor mean,
Tensor var,
double eps,
double output_scale,
long output_zero_point) |
static Tensor |
torch.quantized_gru_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
Tensor b_ih,
Tensor b_hh,
Tensor packed_ih,
Tensor packed_hh,
Tensor col_offsets_ih,
Tensor col_offsets_hh,
Scalar scale_ih,
Scalar scale_hh,
Scalar zero_point_ih,
Scalar zero_point_hh) |
static TensorTensorTuple |
torch.quantized_lstm_cell(Tensor input,
TensorArrayRef hx,
Tensor w_ih,
Tensor w_hh,
Tensor b_ih,
Tensor b_hh,
Tensor packed_ih,
Tensor packed_hh,
Tensor col_offsets_ih,
Tensor col_offsets_hh,
Scalar scale_ih,
Scalar scale_hh,
Scalar zero_point_ih,
Scalar zero_point_hh) |
static Tensor |
torch.quantized_max_pool1d(Tensor self,
long... kernel_size) |
static Tensor |
torch.quantized_max_pool1d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.quantized_max_pool1d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.quantized_max_pool1d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.quantized_max_pool2d(Tensor self,
long... kernel_size) |
static Tensor |
torch.quantized_max_pool2d(Tensor self,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
boolean ceil_mode) |
static Tensor |
torch.quantized_max_pool2d(Tensor self,
LongArrayRef kernel_size) |
static Tensor |
torch.quantized_max_pool2d(Tensor self,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
boolean ceil_mode) |
static Tensor |
torch.quantized_rnn_relu_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
Tensor b_ih,
Tensor b_hh,
Tensor packed_ih,
Tensor packed_hh,
Tensor col_offsets_ih,
Tensor col_offsets_hh,
Scalar scale_ih,
Scalar scale_hh,
Scalar zero_point_ih,
Scalar zero_point_hh) |
static Tensor |
torch.quantized_rnn_tanh_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
Tensor b_ih,
Tensor b_hh,
Tensor packed_ih,
Tensor packed_hh,
Tensor col_offsets_ih,
Tensor col_offsets_hh,
Scalar scale_ih,
Scalar scale_hh,
Scalar zero_point_ih,
Scalar zero_point_hh) |
static Tensor |
torch.rad2deg_(Tensor self) |
static Tensor |
torch.rad2deg_out(Tensor out,
Tensor self) |
static Tensor |
torch.rad2deg_outf(Tensor self,
Tensor out) |
static Tensor |
torch.rad2deg(Tensor self) |
static Tensor |
torch.rand_like(Tensor self) |
static Tensor |
torch.rand_like(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.rand_like(Tensor self,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.rand_out(Tensor out,
long... size) |
static Tensor |
torch.rand_out(Tensor out,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.rand_out(Tensor out,
LongArrayRef size) |
static Tensor |
torch.rand_out(Tensor out,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.rand_outf(long[] size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.rand_outf(long[] size,
Tensor out) |
static Tensor |
torch.rand_outf(LongArrayRef size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.rand_outf(LongArrayRef size,
Tensor out) |
static Tensor |
torch.randint_like(Tensor self,
long high) |
static Tensor |
torch.randint_like(Tensor self,
long low,
long high) |
static Tensor |
torch.randint_like(Tensor self,
long low,
long high,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randint_like(Tensor self,
long low,
long high,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randint_like(Tensor self,
long high,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randint_like(Tensor self,
long high,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randint_out(Tensor out,
long high,
long... size) |
static Tensor |
torch.randint_out(Tensor out,
long high,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.randint_out(Tensor out,
long high,
LongArrayRef size) |
static Tensor |
torch.randint_out(Tensor out,
long high,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.randint_out(Tensor out,
long low,
long high,
long... size) |
static Tensor |
torch.randint_out(Tensor out,
long low,
long high,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.randint_out(Tensor out,
long low,
long high,
LongArrayRef size) |
static Tensor |
torch.randint_out(Tensor out,
long low,
long high,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.randint_outf(long high,
long[] size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randint_outf(long high,
long[] size,
Tensor out) |
static Tensor |
torch.randint_outf(long high,
LongArrayRef size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randint_outf(long high,
LongArrayRef size,
Tensor out) |
static Tensor |
torch.randint_outf(long low,
long high,
long[] size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randint_outf(long low,
long high,
long[] size,
Tensor out) |
static Tensor |
torch.randint_outf(long low,
long high,
LongArrayRef size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randint_outf(long low,
long high,
LongArrayRef size,
Tensor out) |
static Tensor |
torch.randn_like(Tensor self) |
static Tensor |
torch.randn_like(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randn_like(Tensor self,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.randn_out(Tensor out,
long... size) |
static Tensor |
torch.randn_out(Tensor out,
long[] size,
GeneratorOptional generator) |
static Tensor |
torch.randn_out(Tensor out,
LongArrayRef size) |
static Tensor |
torch.randn_out(Tensor out,
LongArrayRef size,
GeneratorOptional generator) |
static Tensor |
torch.randn_outf(long[] size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randn_outf(long[] size,
Tensor out) |
static Tensor |
torch.randn_outf(LongArrayRef size,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randn_outf(LongArrayRef size,
Tensor out) |
static Tensor |
torch.randperm_out(Tensor out,
long n) |
static Tensor |
torch.randperm_out(Tensor out,
long n,
GeneratorOptional generator) |
static Tensor |
torch.randperm_outf(long n,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.randperm_outf(long n,
Tensor out) |
static Tensor |
torch.range_out(Tensor out,
Scalar start,
Scalar end) |
static Tensor |
torch.range_out(Tensor out,
Scalar start,
Scalar end,
Scalar step) |
static Tensor |
torch.range_outf(Scalar start,
Scalar end,
Scalar step,
Tensor out) |
static Tensor |
torch.ravel(Tensor self) |
static Tensor |
torch.real(Tensor self) |
static void |
torch.rebase_history(Tensor arg0,
Edge gradient_edge)
Update the
grad_fn of an existing Variable. |
static Tensor |
torch.reciprocal_(Tensor self) |
static Tensor |
torch.reciprocal_out(Tensor out,
Tensor self) |
static Tensor |
torch.reciprocal_outf(Tensor self,
Tensor out) |
static Tensor |
torch.reciprocal(Tensor self) |
static void |
torch.recordTensorIndex(Tensor tensor,
TensorVector outIndices,
long[] dim_ptr) |
static void |
torch.recordTensorIndex(Tensor tensor,
TensorVector outIndices,
LongBuffer dim_ptr) |
static void |
torch.recordTensorIndex(Tensor tensor,
TensorVector outIndices,
LongPointer dim_ptr) |
static Tensor |
torch.reflection_pad1d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad1d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad1d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad1d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad1d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad1d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad1d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad1d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad1d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.reflection_pad1d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.reflection_pad1d(Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad1d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad2d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad2d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad2d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad2d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad2d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad2d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad2d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.reflection_pad2d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.reflection_pad2d(Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad2d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad3d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad3d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.reflection_pad3d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad3d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad3d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad3d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reflection_pad3d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.reflection_pad3d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.reflection_pad3d(Tensor self,
long... padding) |
static Tensor |
torch.reflection_pad3d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.relu_(Tensor self) |
static Tensor |
torch.relu(Tensor self) |
static Tensor |
torch.relu(Tensor input,
boolean inplace) |
static Tensor |
torch.relu(Tensor input,
ReLUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.relu
/** about the exact behavior of this functional.
|
static Tensor |
torch.relu6_(Tensor self) |
static Tensor |
torch.relu6(Tensor self) |
static Tensor |
torch.relu6(Tensor input,
boolean inplace) |
static Tensor |
torch.relu6(Tensor input,
ReLU6Options options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.relu6
/** about the exact behavior of this functional.
|
static Tensor |
torch.remainder_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.remainder_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.remainder_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.remainder_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.remainder(Scalar self,
Tensor other) |
static Tensor |
torch.remainder(Tensor self,
Scalar other) |
static Tensor |
torch.remainder(Tensor self,
Tensor other) |
static Tensor |
torch.renorm_out(Tensor out,
Tensor self,
Scalar p,
long dim,
Scalar maxnorm) |
static Tensor |
torch.renorm_outf(Tensor self,
Scalar p,
long dim,
Scalar maxnorm,
Tensor out) |
static Tensor |
torch.renorm(Tensor self,
Scalar p,
long dim,
Scalar maxnorm) |
static Tensor |
torch.repeat_interleave(Tensor repeats) |
static Tensor |
torch.repeat_interleave(Tensor self,
long repeats) |
static Tensor |
torch.repeat_interleave(Tensor self,
long repeats,
LongOptional dim,
LongOptional output_size) |
static Tensor |
torch.repeat_interleave(Tensor repeats,
LongOptional output_size) |
static Tensor |
torch.repeat_interleave(Tensor self,
Tensor repeats) |
static Tensor |
torch.repeat_interleave(Tensor self,
Tensor repeats,
LongOptional dim,
LongOptional output_size) |
static Tensor |
torch.replication_pad1d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad1d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad1d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad1d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad1d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad1d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad1d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad1d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad1d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.replication_pad1d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.replication_pad1d(Tensor self,
long... padding) |
static Tensor |
torch.replication_pad1d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad2d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad2d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad2d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad2d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad2d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad2d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad2d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad2d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.replication_pad2d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.replication_pad2d(Tensor self,
long... padding) |
static Tensor |
torch.replication_pad2d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad3d_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad3d_backward_outf(Tensor grad_output,
Tensor self,
long[] padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad3d_backward_outf(Tensor grad_output,
Tensor self,
LongArrayRef padding,
Tensor grad_input) |
static Tensor |
torch.replication_pad3d_backward(Tensor grad_output,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad3d_backward(Tensor grad_output,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad3d_out(Tensor out,
Tensor self,
long... padding) |
static Tensor |
torch.replication_pad3d_out(Tensor out,
Tensor self,
LongArrayRef padding) |
static Tensor |
torch.replication_pad3d_outf(Tensor self,
long[] padding,
Tensor out) |
static Tensor |
torch.replication_pad3d_outf(Tensor self,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.replication_pad3d(Tensor self,
long... padding) |
static Tensor |
torch.replication_pad3d(Tensor self,
LongArrayRef padding) |
static Tensor |
torch.reshape(Tensor self,
long... shape) |
static Tensor |
torch.reshape(Tensor self,
LongArrayRef shape) |
static Tensor |
torch.resize_as_(Tensor self,
Tensor the_template) |
static Tensor |
torch.resize_as_(Tensor self,
Tensor the_template,
MemoryFormatOptional memory_format) |
static Tensor |
torch.resize_as_sparse_(Tensor self,
Tensor the_template) |
static Tensor |
torch.resolve_conj(Tensor self) |
static Tensor |
torch.resolve_neg(Tensor self) |
static torch.ScalarType |
torch.result_type(Scalar scalar,
Tensor tensor) |
static torch.ScalarType |
torch.result_type(Tensor tensor,
Scalar other) |
static torch.ScalarType |
torch.result_type(Tensor tensor,
Tensor other) |
static Tensor |
torch.rnn_relu_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh) |
static Tensor |
torch.rnn_relu_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static TensorTensorTuple |
torch.rnn_relu(Tensor input,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static TensorTensorTuple |
torch.rnn_relu(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static Tensor |
torch.rnn_tanh_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh) |
static Tensor |
torch.rnn_tanh_cell(Tensor input,
Tensor hx,
Tensor w_ih,
Tensor w_hh,
TensorOptional b_ih,
TensorOptional b_hh) |
static TensorTensorTuple |
torch.rnn_tanh(Tensor input,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional,
boolean batch_first) |
static TensorTensorTuple |
torch.rnn_tanh(Tensor data,
Tensor batch_sizes,
Tensor hx,
TensorArrayRef params,
boolean has_biases,
long num_layers,
double dropout,
boolean train,
boolean bidirectional) |
static Tensor |
torch.roll(Tensor self,
long... shifts) |
static Tensor |
torch.roll(Tensor self,
long[] shifts,
long... dims) |
static Tensor |
torch.roll(Tensor self,
LongArrayRef shifts) |
static Tensor |
torch.roll(Tensor self,
LongArrayRef shifts,
LongArrayRef dims) |
static Tensor |
torch.rot90(Tensor self) |
static Tensor |
torch.rot90(Tensor self,
long k,
long... dims) |
static Tensor |
torch.rot90(Tensor self,
long k,
LongArrayRef dims) |
static Tensor |
torch.round_(Tensor self) |
static Tensor |
torch.round_out(Tensor out,
Tensor self) |
static Tensor |
torch.round_outf(Tensor self,
Tensor out) |
static Tensor |
torch.round(Tensor self) |
static Tensor |
torch.row_stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.row_stack_outf(TensorArrayRef tensors,
Tensor out) |
static Tensor |
torch.rrelu_(Tensor self) |
static Tensor |
torch.rrelu_(Tensor self,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator) |
static Tensor |
torch.rrelu_with_noise_(Tensor self,
Tensor noise) |
static Tensor |
torch.rrelu_with_noise_(Tensor self,
Tensor noise,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator) |
static Tensor |
torch.rrelu_with_noise_backward(Tensor grad_output,
Tensor self,
Tensor noise,
Scalar lower,
Scalar upper,
boolean training,
boolean self_is_result) |
static Tensor |
torch.rrelu_with_noise_out(Tensor out,
Tensor self,
Tensor noise) |
static Tensor |
torch.rrelu_with_noise_out(Tensor out,
Tensor self,
Tensor noise,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator) |
static Tensor |
torch.rrelu_with_noise_outf(Tensor self,
Tensor noise,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator,
Tensor out) |
static Tensor |
torch.rrelu_with_noise(Tensor self,
Tensor noise) |
static Tensor |
torch.rrelu_with_noise(Tensor self,
Tensor noise,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator) |
static Tensor |
torch.rrelu(Tensor self) |
static Tensor |
torch.rrelu(Tensor input,
double lower,
double upper,
boolean training,
boolean inplace) |
static Tensor |
torch.rrelu(Tensor input,
RReLUFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.rrelu
/** about the exact behavior of this functional.
|
static Tensor |
torch.rrelu(Tensor self,
Scalar lower,
Scalar upper,
boolean training,
GeneratorOptional generator) |
static Tensor |
torch.rsqrt_(Tensor self) |
static Tensor |
torch.rsqrt_out(Tensor out,
Tensor self) |
static Tensor |
torch.rsqrt_outf(Tensor self,
Tensor out) |
static Tensor |
torch.rsqrt(Tensor self) |
static Tensor |
torch.rsub(Tensor self,
Scalar other) |
static Tensor |
torch.rsub(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch.rsub(Tensor self,
Tensor other) |
static Tensor |
torch.rsub(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.scatter_add_out(Tensor out,
Tensor self,
long dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter_add_outf(Tensor self,
long dim,
Tensor index,
Tensor src,
Tensor out) |
static Tensor |
torch.scatter_add(Tensor self,
Dimname dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter_add(Tensor self,
long dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter_out(Tensor out,
Tensor self,
long dim,
Tensor index,
Scalar value) |
static Tensor |
torch.scatter_out(Tensor out,
Tensor self,
long dim,
Tensor index,
Scalar value,
Pointer reduce) |
static Tensor |
torch.scatter_out(Tensor out,
Tensor self,
long dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter_out(Tensor out,
Tensor self,
long dim,
Tensor index,
Tensor src,
Pointer reduce) |
static Tensor |
torch.scatter_outf(Tensor self,
long dim,
Tensor index,
Scalar value,
Pointer reduce,
Tensor out) |
static Tensor |
torch.scatter_outf(Tensor self,
long dim,
Tensor index,
Scalar value,
Tensor out) |
static Tensor |
torch.scatter_outf(Tensor self,
long dim,
Tensor index,
Tensor src,
Pointer reduce,
Tensor out) |
static Tensor |
torch.scatter_outf(Tensor self,
long dim,
Tensor index,
Tensor src,
Tensor out) |
static Tensor |
torch.scatter(Tensor self,
Dimname dim,
Tensor index,
Scalar value) |
static Tensor |
torch.scatter(Tensor self,
Dimname dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter(Tensor self,
long dim,
Tensor index,
Scalar value) |
static Tensor |
torch.scatter(Tensor self,
long dim,
Tensor index,
Scalar value,
Pointer reduce) |
static Tensor |
torch.scatter(Tensor self,
long dim,
Tensor index,
Tensor src) |
static Tensor |
torch.scatter(Tensor self,
long dim,
Tensor index,
Tensor src,
Pointer reduce) |
static Tensor |
torch.searchsorted_out(Tensor out,
Tensor sorted_sequence,
Tensor self) |
static Tensor |
torch.searchsorted_out(Tensor out,
Tensor sorted_sequence,
Tensor self,
boolean out_int32,
boolean right) |
static Tensor |
torch.searchsorted_outf(Tensor sorted_sequence,
Tensor self,
boolean out_int32,
boolean right,
Tensor out) |
static Tensor |
torch.searchsorted(Tensor sorted_sequence,
Scalar self) |
static Tensor |
torch.searchsorted(Tensor sorted_sequence,
Scalar self,
boolean out_int32,
boolean right) |
static Tensor |
torch.searchsorted(Tensor sorted_sequence,
Tensor self) |
static Tensor |
torch.searchsorted(Tensor sorted_sequence,
Tensor self,
boolean out_int32,
boolean right) |
static Tensor |
torch.segment_reduce(Tensor data,
Pointer reduce) |
static Tensor |
torch.segment_reduce(Tensor data,
Pointer reduce,
TensorOptional lengths,
TensorOptional indices,
long axis,
boolean unsafe,
ScalarOptional initial) |
static Tensor |
torch.select_backward(Tensor grad_output,
long[] input_sizes,
long dim,
long index) |
static Tensor |
torch.select_backward(Tensor grad_output,
LongArrayRef input_sizes,
long dim,
long index) |
static Tensor |
torch.select(Tensor self,
Dimname dim,
long index) |
static Tensor |
torch.select(Tensor self,
long dim,
long index) |
static Tensor |
torch.selu_(Tensor self) |
static Tensor |
torch.selu(Tensor self) |
static Tensor |
torch.selu(Tensor input,
boolean inplace) |
static Tensor |
torch.selu(Tensor input,
SELUOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.selu
/** about the exact behavior of this functional.
|
static void |
torch.set_gradient_edge(Tensor arg0,
Edge edge)
Set the gradient edge -- i.e.
|
static void |
torch.set_item(Tensor self,
TensorIndexArrayRef indices,
Tensor value) |
static void |
torch.set_item(Tensor self,
TensorIndexArrayRef indices,
Tensor value,
boolean disable_slice_optimization) |
static void |
torch.set_name(Tensor arg0,
BytePointer name) |
static void |
torch.set_name(Tensor arg0,
String name) |
static void |
torch.set_version_counter(Tensor arg0,
VariableVersion version_counter) |
static void |
torch.setOutput(Value value,
Tensor output) |
static Tensor |
torch.sgn_out(Tensor out,
Tensor self) |
static Tensor |
torch.sgn_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sgn(Tensor self) |
static OutputArchive |
torch.shiftLeft(OutputArchive archive,
Tensor tensor)
Deserializes the given
tensor_vec of type std::vector<torch::Tensor>. |
static Pointer |
torch.shiftLeft(Pointer out,
Tensor t) |
static InputArchive |
torch.shiftRight(InputArchive archive,
Tensor tensor) |
static Tensor |
torch.sigmoid_(Tensor self) |
static Tensor |
torch.sigmoid_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor output) |
static Tensor |
torch.sigmoid_backward_outf(Tensor grad_output,
Tensor output,
Tensor grad_input) |
static Tensor |
torch.sigmoid_backward(Tensor grad_output,
Tensor output) |
static Tensor |
torch.sigmoid_out(Tensor out,
Tensor self) |
static Tensor |
torch.sigmoid_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sigmoid(Tensor self) |
static Tensor |
torch.sign_out(Tensor out,
Tensor self) |
static Tensor |
torch.sign_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sign(Tensor self) |
static Tensor |
torch.signbit_out(Tensor out,
Tensor self) |
static Tensor |
torch.signbit_outf(Tensor self,
Tensor out) |
static Tensor |
torch.signbit(Tensor self) |
static Tensor |
torch.silu_(Tensor self) |
static Tensor |
torch.silu_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self) |
static Tensor |
torch.silu_backward_outf(Tensor grad_output,
Tensor self,
Tensor grad_input) |
static Tensor |
torch.silu_backward(Tensor grad_output,
Tensor self) |
static Tensor |
torch.silu_out(Tensor out,
Tensor self) |
static Tensor |
torch.silu_outf(Tensor self,
Tensor out) |
static Tensor |
torch.silu(Tensor self) |
static Tensor |
torch.sin_(Tensor self) |
static Tensor |
torch.sin_out(Tensor out,
Tensor self) |
static Tensor |
torch.sin_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sin(Tensor self) |
static Tensor |
torch.sinc_(Tensor self) |
static Tensor |
torch.sinc_out(Tensor out,
Tensor self) |
static Tensor |
torch.sinc_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sinc(Tensor self) |
static Tensor |
torch.sinh_(Tensor self) |
static Tensor |
torch.sinh_out(Tensor out,
Tensor self) |
static Tensor |
torch.sinh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sinh(Tensor self) |
static long |
torch.size(Tensor self,
Dimname dim) |
static long |
torch.size(Tensor tensor,
long dim) |
static Tensor |
torch.slice_backward(Tensor grad_output,
long[] input_sizes,
long dim,
long start,
long end,
long step) |
static Tensor |
torch.slice_backward(Tensor grad_output,
LongArrayRef input_sizes,
long dim,
long start,
long end,
long step) |
static Tensor |
torch.slice(Tensor self) |
static Tensor |
torch.slice(Tensor self,
long dim,
LongOptional start,
LongOptional end,
long step) |
static TensorTensorTuple |
torch.slogdet(Tensor self) |
static TensorTensorTensorTuple |
torch.slow_conv_dilated2d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.slow_conv_dilated2d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
BoolPointer output_mask) |
static Tensor |
torch.slow_conv_dilated2d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_dilated2d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long... dilation) |
static Tensor |
torch.slow_conv_dilated2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_dilated2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static TensorTensorTensorTuple |
torch.slow_conv_dilated3d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] dilation,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.slow_conv_dilated3d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation,
BoolPointer output_mask) |
static Tensor |
torch.slow_conv_dilated3d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_dilated3d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long... dilation) |
static Tensor |
torch.slow_conv_dilated3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_dilated3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef dilation) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor columns,
Tensor ones) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose2d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor columns,
Tensor ones) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor columns,
Tensor ones,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose2d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor columns,
Tensor ones,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static TensorTensorTensorTuple |
torch.slow_conv_transpose2d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor columns,
Tensor ones,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.slow_conv_transpose2d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor columns,
Tensor ones,
BoolPointer output_mask) |
static Tensor |
torch.slow_conv_transpose2d_out(Tensor out,
Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_transpose2d_out(Tensor out,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long... dilation) |
static Tensor |
torch.slow_conv_transpose2d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_transpose2d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation) |
static Tensor |
torch.slow_conv_transpose2d_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor out) |
static Tensor |
torch.slow_conv_transpose2d_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor out) |
static Tensor |
torch.slow_conv_transpose2d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_transpose2d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long... dilation) |
static Tensor |
torch.slow_conv_transpose2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_transpose2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor finput,
Tensor fgrad_input,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.slow_conv_transpose3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor finput,
Tensor fgrad_input,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static TensorTensorTensorTuple |
torch.slow_conv_transpose3d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor finput,
Tensor fgrad_input,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.slow_conv_transpose3d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor finput,
Tensor fgrad_input,
BoolPointer output_mask) |
static Tensor |
torch.slow_conv_transpose3d_out(Tensor out,
Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_transpose3d_out(Tensor out,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long... dilation) |
static Tensor |
torch.slow_conv_transpose3d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_transpose3d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation) |
static Tensor |
torch.slow_conv_transpose3d_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long[] dilation,
Tensor out) |
static Tensor |
torch.slow_conv_transpose3d_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation,
Tensor out) |
static Tensor |
torch.slow_conv_transpose3d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv_transpose3d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
long[] output_padding,
long... dilation) |
static Tensor |
torch.slow_conv_transpose3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv_transpose3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
LongArrayRef output_padding,
LongArrayRef dilation) |
static PointerPointer<Tensor> |
torch.slow_conv3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.slow_conv3d_backward_out(Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias,
Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.slow_conv3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
Tensor finput,
Tensor fgrad_input,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static PointerPointer<Tensor> |
torch.slow_conv3d_backward_outf(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor finput,
Tensor fgrad_input,
Tensor grad_input,
Tensor grad_weight,
Tensor grad_bias) |
static TensorTensorTensorTuple |
torch.slow_conv3d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
long[] kernel_size,
long[] stride,
long[] padding,
Tensor finput,
Tensor fgrad_input,
BoolPointer output_mask) |
static TensorTensorTensorTuple |
torch.slow_conv3d_backward(Tensor grad_output,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
LongArrayRef stride,
LongArrayRef padding,
Tensor finput,
Tensor fgrad_input,
BoolPointer output_mask) |
static PointerPointer<Tensor> |
torch.slow_conv3d_forward_out(Tensor output,
Tensor finput,
Tensor fgrad_input,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static PointerPointer<Tensor> |
torch.slow_conv3d_forward_out(Tensor output,
Tensor finput,
Tensor fgrad_input,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static PointerPointer<Tensor> |
torch.slow_conv3d_forward_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
Tensor output,
Tensor finput,
Tensor fgrad_input) |
static PointerPointer<Tensor> |
torch.slow_conv3d_forward_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
Tensor output,
Tensor finput,
Tensor fgrad_input) |
static TensorTensorTensorTuple |
torch.slow_conv3d_forward(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static TensorTensorTensorTuple |
torch.slow_conv3d_forward(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.slow_conv3d_out(Tensor out,
Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv3d_out(Tensor out,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static Tensor |
torch.slow_conv3d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv3d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.slow_conv3d_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
Tensor out) |
static Tensor |
torch.slow_conv3d_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.slow_conv3d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.slow_conv3d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static Tensor |
torch.slow_conv3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.slow_conv3d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.smm(Tensor self,
Tensor mat2) |
static Tensor |
torch.smooth_l1_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double beta) |
static Tensor |
torch.smooth_l1_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double beta,
Tensor grad_input) |
static Tensor |
torch.smooth_l1_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
double beta) |
static Tensor |
torch.smooth_l1_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.smooth_l1_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction,
double beta) |
static Tensor |
torch.smooth_l1_loss_outf(Tensor self,
Tensor target,
long reduction,
double beta,
Tensor out) |
static Tensor |
torch.smooth_l1_loss(Tensor self,
Tensor target) |
static Tensor |
torch.smooth_l1_loss(Tensor self,
Tensor target,
long reduction,
double beta) |
static Tensor |
torch.smooth_l1_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.smooth_l1_loss(Tensor input,
Tensor target,
loss_reduction_t reduction,
double beta) |
static Tensor |
torch.smooth_l1_loss(Tensor input,
Tensor target,
SmoothL1LossOptions options,
double beta)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.smooth_l1_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.soft_margin_loss_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.soft_margin_loss_backward_outf(Tensor grad_output,
Tensor self,
Tensor target,
long reduction,
Tensor grad_input) |
static Tensor |
torch.soft_margin_loss_backward(Tensor grad_output,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.soft_margin_loss_out(Tensor out,
Tensor self,
Tensor target) |
static Tensor |
torch.soft_margin_loss_out(Tensor out,
Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.soft_margin_loss_outf(Tensor self,
Tensor target,
long reduction,
Tensor out) |
static Tensor |
torch.soft_margin_loss(Tensor self,
Tensor target) |
static Tensor |
torch.soft_margin_loss(Tensor self,
Tensor target,
long reduction) |
static Tensor |
torch.soft_margin_loss(Tensor input,
Tensor target,
loss_reduction_t reduction) |
static Tensor |
torch.soft_margin_loss(Tensor input,
Tensor target,
SoftMarginLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.soft_margin_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.softmax(Tensor self,
Dimname dim) |
static Tensor |
torch.softmax(Tensor self,
Dimname dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.softmax(Tensor self,
long dim) |
static Tensor |
torch.softmax(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.softmax(Tensor input,
SoftmaxFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softmax
/** about the exact behavior of this functional.
|
static Tensor |
torch.softmin(Tensor input,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.softmin(Tensor input,
SoftminFuncOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softmin
/** about the exact behavior of this functional.
|
static Tensor |
torch.softplus_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Scalar beta,
Scalar threshold,
Tensor output) |
static Tensor |
torch.softplus_backward_outf(Tensor grad_output,
Tensor self,
Scalar beta,
Scalar threshold,
Tensor output,
Tensor grad_input) |
static Tensor |
torch.softplus_backward(Tensor grad_output,
Tensor self,
Scalar beta,
Scalar threshold,
Tensor output) |
static Tensor |
torch.softplus_out(Tensor out,
Tensor self) |
static Tensor |
torch.softplus_out(Tensor out,
Tensor self,
Scalar beta,
Scalar threshold) |
static Tensor |
torch.softplus_outf(Tensor self,
Scalar beta,
Scalar threshold,
Tensor out) |
static Tensor |
torch.softplus(Tensor self) |
static Tensor |
torch.softplus(Tensor input,
double beta,
double threshold) |
static Tensor |
torch.softplus(Tensor self,
Scalar beta,
Scalar threshold) |
static Tensor |
torch.softplus(Tensor input,
SoftplusOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softplus
/** about the exact behavior of this functional.
|
static Tensor |
torch.softshrink_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Scalar lambd) |
static Tensor |
torch.softshrink_backward_outf(Tensor grad_output,
Tensor self,
Scalar lambd,
Tensor grad_input) |
static Tensor |
torch.softshrink_backward(Tensor grad_output,
Tensor self,
Scalar lambd) |
static Tensor |
torch.softshrink_out(Tensor out,
Tensor self) |
static Tensor |
torch.softshrink_out(Tensor out,
Tensor self,
Scalar lambd) |
static Tensor |
torch.softshrink_outf(Tensor self,
Scalar lambd,
Tensor out) |
static Tensor |
torch.softshrink(Tensor self) |
static Tensor |
torch.softshrink(Tensor input,
double lambda) |
static Tensor |
torch.softshrink(Tensor self,
Scalar lambd) |
static Tensor |
torch.softshrink(Tensor input,
SoftshrinkOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.softshrink
/** about the exact behavior of this functional.
|
static Tensor |
torch.softsign(Tensor input) |
static PointerPointer<Tensor> |
torch.solve_out(Tensor solution,
Tensor lu,
Tensor self,
Tensor A) |
static PointerPointer<Tensor> |
torch.solve_outf(Tensor self,
Tensor A,
Tensor solution,
Tensor lu) |
static TensorTensorTuple |
torch.solve(Tensor self,
Tensor A) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable,
Dimname dim) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable,
Dimname dim,
boolean descending) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
BoolOptional stable,
long dim,
boolean descending) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
Dimname dim,
boolean descending) |
static PointerPointer<Tensor> |
torch.sort_out(Tensor values,
Tensor indices,
Tensor self,
long dim,
boolean descending) |
static PointerPointer<Tensor> |
torch.sort_outf(Tensor self,
BoolOptional stable,
Dimname dim,
boolean descending,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.sort_outf(Tensor self,
BoolOptional stable,
long dim,
boolean descending,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.sort_outf(Tensor self,
Dimname dim,
boolean descending,
Tensor values,
Tensor indices) |
static PointerPointer<Tensor> |
torch.sort_outf(Tensor self,
long dim,
boolean descending,
Tensor values,
Tensor indices) |
static TensorTensorTuple |
torch.sort(Tensor self) |
static TensorTensorTuple |
torch.sort(Tensor self,
BoolOptional stable) |
static TensorTensorTuple |
torch.sort(Tensor self,
BoolOptional stable,
Dimname dim) |
static TensorTensorTuple |
torch.sort(Tensor self,
BoolOptional stable,
Dimname dim,
boolean descending) |
static TensorTensorTuple |
torch.sort(Tensor self,
BoolOptional stable,
long dim,
boolean descending) |
static TensorTensorTuple |
torch.sort(Tensor self,
Dimname dim) |
static TensorTensorTuple |
torch.sort(Tensor self,
Dimname dim,
boolean descending) |
static TensorTensorTuple |
torch.sort(Tensor self,
long dim,
boolean descending) |
static Tensor |
torch.sparse_(Tensor tensor,
double sparsity) |
static Tensor |
torch.sparse_(Tensor tensor,
double sparsity,
double std)
Fills the 2D input
Tensor as a sparse matrix, where the
non-zero elements will be drawn from a centered normal distribution
with the given standard deviation std, as described in "Deep learning via
Hessian-free optimization" - Martens, J. |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
long... size) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
long[] size,
TensorOptions options) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
LongArrayRef size) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_coo_tensor(Tensor indices,
Tensor values,
TensorOptions options) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long[] size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
long[] size,
TensorOptions options) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
LongArrayRef size,
TensorOptions options) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory) |
static Tensor |
torch.sparse_csr_tensor(Tensor crow_indices,
Tensor col_indices,
Tensor values,
TensorOptions options) |
static Tensor |
torch.special_digamma_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_digamma_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_digamma(Tensor self) |
static Tensor |
torch.special_entr_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_entr_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_entr(Tensor self) |
static Tensor |
torch.special_erf_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_erf_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_erf(Tensor self) |
static Tensor |
torch.special_erfc_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_erfc_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_erfc(Tensor self) |
static Tensor |
torch.special_erfcx_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_erfcx_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_erfcx(Tensor self) |
static Tensor |
torch.special_erfinv_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_erfinv_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_erfinv(Tensor self) |
static Tensor |
torch.special_exp2_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_exp2_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_exp2(Tensor self) |
static Tensor |
torch.special_expit_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_expit_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_expit(Tensor self) |
static Tensor |
torch.special_expm1_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_expm1_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_expm1(Tensor self) |
static Tensor |
torch.special_gammainc_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.special_gammainc_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_gammainc(Tensor self,
Tensor other) |
static Tensor |
torch.special_gammaincc_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.special_gammaincc_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_gammaincc(Tensor self,
Tensor other) |
static Tensor |
torch.special_gammaln_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_gammaln_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_gammaln(Tensor self) |
static Tensor |
torch.special_i0_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_i0_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_i0(Tensor self) |
static Tensor |
torch.special_i0e_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_i0e_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_i0e(Tensor self) |
static Tensor |
torch.special_i1_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_i1_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_i1(Tensor self) |
static Tensor |
torch.special_i1e_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_i1e_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_i1e(Tensor self) |
static Tensor |
torch.special_log_softmax(Tensor self,
long dim) |
static Tensor |
torch.special_log_softmax(Tensor self,
long dim,
ScalarTypeOptional dtype) |
static Tensor |
torch.special_log1p_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_log1p_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_log1p(Tensor self) |
static Tensor |
torch.special_logit_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_logit_out(Tensor out,
Tensor self,
DoubleOptional eps) |
static Tensor |
torch.special_logit_outf(Tensor self,
DoubleOptional eps,
Tensor out) |
static Tensor |
torch.special_logit(Tensor self) |
static Tensor |
torch.special_logit(Tensor self,
DoubleOptional eps) |
static Tensor |
torch.special_logsumexp_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.special_logsumexp_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.special_logsumexp_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.special_logsumexp_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.special_logsumexp_outf(Tensor self,
long[] dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.special_logsumexp_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
Tensor out) |
static Tensor |
torch.special_logsumexp(Tensor self,
long... dim) |
static Tensor |
torch.special_logsumexp(Tensor self,
long[] dim,
boolean keepdim) |
static Tensor |
torch.special_logsumexp(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.special_logsumexp(Tensor self,
LongArrayRef dim,
boolean keepdim) |
static Tensor |
torch.special_multigammaln_out(Tensor out,
Tensor self,
long p) |
static Tensor |
torch.special_multigammaln_outf(Tensor self,
long p,
Tensor out) |
static Tensor |
torch.special_multigammaln(Tensor self,
long p) |
static Tensor |
torch.special_ndtr_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_ndtr_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_ndtr(Tensor self) |
static Tensor |
torch.special_ndtri_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_ndtri_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_ndtri(Tensor self) |
static Tensor |
torch.special_polygamma_out(Tensor out,
long n,
Tensor self) |
static Tensor |
torch.special_polygamma_outf(long n,
Tensor self,
Tensor out) |
static Tensor |
torch.special_polygamma(long n,
Tensor self) |
static Tensor |
torch.special_psi_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_psi_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_psi(Tensor self) |
static Tensor |
torch.special_round_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_round_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_round(Tensor self) |
static Tensor |
torch.special_sinc_out(Tensor out,
Tensor self) |
static Tensor |
torch.special_sinc_outf(Tensor self,
Tensor out) |
static Tensor |
torch.special_sinc(Tensor self) |
static Tensor |
torch.special_xlog1py_out(Tensor out,
Scalar self,
Tensor other) |
static Tensor |
torch.special_xlog1py_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.special_xlog1py_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.special_xlog1py_outf(Scalar self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_xlog1py_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.special_xlog1py_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_xlog1py(Scalar self,
Tensor other) |
static Tensor |
torch.special_xlog1py(Tensor self,
Scalar other) |
static Tensor |
torch.special_xlog1py(Tensor self,
Tensor other) |
static Tensor |
torch.special_xlogy_out(Tensor out,
Scalar self,
Tensor other) |
static Tensor |
torch.special_xlogy_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.special_xlogy_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.special_xlogy_outf(Scalar self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_xlogy_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.special_xlogy_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_xlogy(Scalar self,
Tensor other) |
static Tensor |
torch.special_xlogy(Tensor self,
Scalar other) |
static Tensor |
torch.special_xlogy(Tensor self,
Tensor other) |
static Tensor |
torch.special_zeta_out(Tensor out,
Scalar self,
Tensor other) |
static Tensor |
torch.special_zeta_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.special_zeta_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.special_zeta_outf(Scalar self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_zeta_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.special_zeta_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.special_zeta(Scalar self,
Tensor other) |
static Tensor |
torch.special_zeta(Tensor self,
Scalar other) |
static Tensor |
torch.special_zeta(Tensor self,
Tensor other) |
static TensorVector |
torch.split_with_sizes(Tensor self,
long... split_sizes) |
static TensorVector |
torch.split_with_sizes(Tensor self,
long[] split_sizes,
long dim) |
static TensorVector |
torch.split_with_sizes(Tensor self,
LongArrayRef split_sizes) |
static TensorVector |
torch.split_with_sizes(Tensor self,
LongArrayRef split_sizes,
long dim) |
static TensorVector |
torch.split(Tensor self,
long split_size) |
static TensorVector |
torch.split(Tensor self,
long split_size,
long dim) |
static Tensor |
torch.sqrt_(Tensor self) |
static Tensor |
torch.sqrt_out(Tensor out,
Tensor self) |
static Tensor |
torch.sqrt_outf(Tensor self,
Tensor out) |
static Tensor |
torch.sqrt(Tensor self) |
static Tensor |
torch.square_(Tensor self) |
static Tensor |
torch.square_out(Tensor out,
Tensor self) |
static Tensor |
torch.square_outf(Tensor self,
Tensor out) |
static Tensor |
torch.square(Tensor self) |
static Tensor |
torch.squeeze(Tensor self) |
static Tensor |
torch.squeeze(Tensor self,
Dimname dim) |
static Tensor |
torch.squeeze(Tensor self,
long dim) |
static Tensor |
torch.sspaddmm_out(Tensor out,
Tensor self,
Tensor mat1,
Tensor mat2) |
static Tensor |
torch.sspaddmm_out(Tensor out,
Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.sspaddmm_outf(Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha,
Tensor out) |
static Tensor |
torch.sspaddmm(Tensor self,
Tensor mat1,
Tensor mat2) |
static Tensor |
torch.sspaddmm(Tensor self,
Tensor mat1,
Tensor mat2,
Scalar beta,
Scalar alpha) |
static Tensor |
torch.stack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.stack_out(Tensor out,
TensorArrayRef tensors,
long dim) |
static Tensor |
torch.stack_outf(TensorArrayRef tensors,
long dim,
Tensor out) |
static TensorTensorTuple |
torch.std_mean(Tensor self) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
boolean unbiased) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
DimnameArrayRef dim) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
DimnameArrayRef dim,
LongOptional correction) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
int dim) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
long... dim) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
LongArrayRef dim) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
LongArrayRefOptional dim,
LongOptional correction) |
static TensorTensorTuple |
torch.std_mean(Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
LongOptional correction) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
LongArrayRefOptional dim,
LongOptional correction) |
static Tensor |
torch.std_out(Tensor out,
Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.std_outf(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.std_outf(Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim,
Tensor out) |
static Tensor |
torch.std_outf(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.std_outf(Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.std_outf(Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim,
Tensor out) |
static Tensor |
torch.std(Tensor self) |
static Tensor |
torch.std(Tensor self,
boolean unbiased) |
static Tensor |
torch.std(Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.std(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std(Tensor self,
DimnameArrayRef dim,
LongOptional correction) |
static Tensor |
torch.std(Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.std(Tensor self,
int dim) |
static Tensor |
torch.std(Tensor self,
long... dim) |
static Tensor |
torch.std(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.std(Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.std(Tensor self,
LongArrayRefOptional dim,
LongOptional correction) |
static Tensor |
torch.std(Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.stft(Tensor self,
long n_fft) |
static Tensor |
torch.stft(Tensor self,
long n_fft,
LongOptional hop_length,
LongOptional win_length,
TensorOptional window,
boolean normalized,
BoolOptional onesided,
BoolOptional return_complex) |
static long |
torch.stride(Tensor self,
Dimname dim) |
static long |
torch.stride(Tensor tensor,
long dim) |
static Tensor |
torch.sub_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.sub_out(Tensor out,
Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.sub_outf(Tensor self,
Tensor other,
Scalar alpha,
Tensor out) |
static Tensor |
torch.sub(Tensor self,
Scalar other) |
static Tensor |
torch.sub(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch.sub(Tensor self,
Tensor other) |
static Tensor |
torch.sub(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.subtract_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.subtract_out(Tensor out,
Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.subtract_outf(Tensor self,
Tensor other,
Scalar alpha,
Tensor out) |
static Tensor |
torch.subtract(Scalar x,
Tensor y) |
static Tensor |
torch.subtract(Tensor self,
Scalar other) |
static Tensor |
torch.subtract(Tensor self,
Scalar other,
Scalar alpha) |
static Tensor |
torch.subtract(Tensor self,
Tensor other) |
static Tensor |
torch.subtract(Tensor self,
Tensor other,
Scalar alpha) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.sum_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum_outf(Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.sum_outf(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.sum_outf(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype,
Tensor out) |
static Tensor |
torch.sum_to(Tensor tensor,
long... shape) |
static Tensor |
torch.sum_to(Tensor tensor,
LongArrayRef shape) |
static Tensor |
torch.sum(Tensor self) |
static Tensor |
torch.sum(Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.sum(Tensor self,
DimnameArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum(Tensor self,
long... dim) |
static Tensor |
torch.sum(Tensor self,
long[] dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.sum(Tensor self,
LongArrayRef dim,
boolean keepdim,
ScalarTypeOptional dtype) |
static Tensor |
torch.sum(Tensor self,
ScalarTypeOptional dtype) |
static PointerPointer<Tensor> |
torch.svd_out(Tensor U,
Tensor S,
Tensor V,
Tensor self) |
static PointerPointer<Tensor> |
torch.svd_out(Tensor U,
Tensor S,
Tensor V,
Tensor self,
boolean some,
boolean compute_uv) |
static PointerPointer<Tensor> |
torch.svd_outf(Tensor self,
boolean some,
boolean compute_uv,
Tensor U,
Tensor S,
Tensor V) |
static TensorTensorTensorTuple |
torch.svd(Tensor self) |
static TensorTensorTensorTuple |
torch.svd(Tensor self,
boolean some,
boolean compute_uv) |
static Tensor |
torch.swapaxes(Tensor self,
long axis0,
long axis1) |
static Tensor |
torch.swapdims(Tensor self,
long dim0,
long dim1) |
static PointerPointer<Tensor> |
torch.symeig_out(Tensor e,
Tensor V,
Tensor self) |
static PointerPointer<Tensor> |
torch.symeig_out(Tensor e,
Tensor V,
Tensor self,
boolean eigenvectors,
boolean upper) |
static PointerPointer<Tensor> |
torch.symeig_outf(Tensor self,
boolean eigenvectors,
boolean upper,
Tensor e,
Tensor V) |
static TensorTensorTuple |
torch.symeig(Tensor self) |
static TensorTensorTuple |
torch.symeig(Tensor self,
boolean eigenvectors,
boolean upper) |
static Tensor |
torch.t(Tensor self) |
static Tensor |
torch.take_along_dim_out(Tensor out,
Tensor self,
Tensor indices) |
static Tensor |
torch.take_along_dim_out(Tensor out,
Tensor self,
Tensor indices,
LongOptional dim) |
static Tensor |
torch.take_along_dim_outf(Tensor self,
Tensor indices,
LongOptional dim,
Tensor out) |
static Tensor |
torch.take_along_dim(Tensor self,
Tensor indices) |
static Tensor |
torch.take_along_dim(Tensor self,
Tensor indices,
LongOptional dim) |
static Tensor |
torch.take_out(Tensor out,
Tensor self,
Tensor index) |
static Tensor |
torch.take_outf(Tensor self,
Tensor index,
Tensor out) |
static Tensor |
torch.take(Tensor self,
Tensor index) |
static Tensor |
torch.tan_(Tensor self) |
static Tensor |
torch.tan_out(Tensor out,
Tensor self) |
static Tensor |
torch.tan_outf(Tensor self,
Tensor out) |
static Tensor |
torch.tan(Tensor self) |
static Tensor |
torch.tanh_(Tensor self) |
static Tensor |
torch.tanh_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor output) |
static Tensor |
torch.tanh_backward_outf(Tensor grad_output,
Tensor output,
Tensor grad_input) |
static Tensor |
torch.tanh_backward(Tensor grad_output,
Tensor output) |
static Tensor |
torch.tanh_out(Tensor out,
Tensor self) |
static Tensor |
torch.tanh_outf(Tensor self,
Tensor out) |
static Tensor |
torch.tanh(Tensor self) |
static Tensor |
torch.tanhshrink(Tensor input) |
static TensorVector |
torch.tensor_split(Tensor self,
long... indices) |
static TensorVector |
torch.tensor_split(Tensor self,
long sections) |
static TensorVector |
torch.tensor_split(Tensor self,
long[] indices,
long dim) |
static TensorVector |
torch.tensor_split(Tensor self,
LongArrayRef indices) |
static TensorVector |
torch.tensor_split(Tensor self,
LongArrayRef indices,
long dim) |
static TensorVector |
torch.tensor_split(Tensor self,
long sections,
long dim) |
static TensorVector |
torch.tensor_split(Tensor self,
Tensor tensor_indices_or_sections) |
static TensorVector |
torch.tensor_split(Tensor self,
Tensor tensor_indices_or_sections,
long dim) |
static Tensor |
torch.tensordot_out(Tensor out,
Tensor self,
Tensor other,
long[] dims_self,
long... dims_other) |
static Tensor |
torch.tensordot_out(Tensor out,
Tensor self,
Tensor other,
LongArrayRef dims_self,
LongArrayRef dims_other) |
static Tensor |
torch.tensordot_outf(Tensor self,
Tensor other,
long[] dims_self,
long[] dims_other,
Tensor out) |
static Tensor |
torch.tensordot_outf(Tensor self,
Tensor other,
LongArrayRef dims_self,
LongArrayRef dims_other,
Tensor out) |
static Tensor |
torch.tensordot(Tensor self,
Tensor other,
long[] dims_self,
long... dims_other) |
static Tensor |
torch.tensordot(Tensor self,
Tensor other,
LongArrayRef dims_self,
LongArrayRef dims_other) |
static TensorType |
torch.tensorTypeInCurrentExecutionContext(Tensor t) |
static Tensor |
torch.thnn_conv2d_out(Tensor out,
Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.thnn_conv2d_out(Tensor out,
Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static Tensor |
torch.thnn_conv2d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.thnn_conv2d_out(Tensor out,
Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.thnn_conv2d_outf(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long[] padding,
Tensor out) |
static Tensor |
torch.thnn_conv2d_outf(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding,
Tensor out) |
static Tensor |
torch.thnn_conv2d(Tensor self,
Tensor weight,
long... kernel_size) |
static Tensor |
torch.thnn_conv2d(Tensor self,
Tensor weight,
long[] kernel_size,
TensorOptional bias,
long[] stride,
long... padding) |
static Tensor |
torch.thnn_conv2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size) |
static Tensor |
torch.thnn_conv2d(Tensor self,
Tensor weight,
LongArrayRef kernel_size,
TensorOptional bias,
LongArrayRef stride,
LongArrayRef padding) |
static Tensor |
torch.threshold_(Tensor self,
Scalar threshold,
Scalar value) |
static Tensor |
torch.threshold_backward_out(Tensor grad_input,
Tensor grad_output,
Tensor self,
Scalar threshold) |
static Tensor |
torch.threshold_backward_outf(Tensor grad_output,
Tensor self,
Scalar threshold,
Tensor grad_input) |
static Tensor |
torch.threshold_backward(Tensor grad_output,
Tensor self,
Scalar threshold) |
static Tensor |
torch.threshold_out(Tensor out,
Tensor self,
Scalar threshold,
Scalar value) |
static Tensor |
torch.threshold_outf(Tensor self,
Scalar threshold,
Scalar value,
Tensor out) |
static Tensor |
torch.threshold(Tensor input,
double threshold,
double value,
boolean inplace) |
static Tensor |
torch.threshold(Tensor self,
Scalar threshold,
Scalar value) |
static Tensor |
torch.threshold(Tensor input,
ThresholdOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.threshold
/** about the exact behavior of this functional.
|
static Tensor |
torch.tile(Tensor self,
long... dims) |
static Tensor |
torch.tile(Tensor self,
LongArrayRef dims) |
static Tensor |
torch.to_dense_backward(Tensor grad,
Tensor input) |
static Tensor |
torch.to_mkldnn_backward(Tensor grad,
Tensor input) |
static TensorOptionalVector |
torch.to_optional(Tensor output)
To use custom autograd operations, implement a Function subclass with
static forward and backward functions:
forward can take as many arguments as you want and should return either a
variable list or a Variable. |
static PointerPointer<Tensor> |
torch.topk_out(Tensor values,
Tensor indices,
Tensor self,
long k) |
static PointerPointer<Tensor> |
torch.topk_out(Tensor values,
Tensor indices,
Tensor self,
long k,
long dim,
boolean largest,
boolean sorted) |
static PointerPointer<Tensor> |
torch.topk_outf(Tensor self,
long k,
long dim,
boolean largest,
boolean sorted,
Tensor values,
Tensor indices) |
static TensorTensorTuple |
torch.topk(Tensor self,
long k) |
static TensorTensorTuple |
torch.topk(Tensor self,
long k,
long dim,
boolean largest,
boolean sorted) |
static Tensor |
torch.trace_backward(Tensor grad,
long... sizes) |
static Tensor |
torch.trace_backward(Tensor grad,
LongArrayRef sizes) |
static Tensor |
torch.trace(Tensor self) |
static Tensor |
torch.transpose(Tensor self,
Dimname dim0,
Dimname dim1) |
static Tensor |
torch.transpose(Tensor self,
long dim0,
long dim1) |
static Tensor |
torch.trapezoid(Tensor y) |
static Tensor |
torch.trapezoid(Tensor y,
Scalar dx,
long dim) |
static Tensor |
torch.trapezoid(Tensor y,
Tensor x) |
static Tensor |
torch.trapezoid(Tensor y,
Tensor x,
long dim) |
static Tensor |
torch.trapz(Tensor y) |
static Tensor |
torch.trapz(Tensor y,
double dx,
long dim) |
static Tensor |
torch.trapz(Tensor y,
Tensor x) |
static Tensor |
torch.trapz(Tensor y,
Tensor x,
long dim) |
static PointerPointer<Tensor> |
torch.triangular_solve_out(Tensor X,
Tensor M,
Tensor self,
Tensor A) |
static PointerPointer<Tensor> |
torch.triangular_solve_out(Tensor X,
Tensor M,
Tensor self,
Tensor A,
boolean upper,
boolean transpose,
boolean unitriangular) |
static PointerPointer<Tensor> |
torch.triangular_solve_outf(Tensor self,
Tensor A,
boolean upper,
boolean transpose,
boolean unitriangular,
Tensor X,
Tensor M) |
static TensorTensorTuple |
torch.triangular_solve(Tensor self,
Tensor A) |
static TensorTensorTuple |
torch.triangular_solve(Tensor self,
Tensor A,
boolean upper,
boolean transpose,
boolean unitriangular) |
static Tensor |
torch.tril_out(Tensor out,
Tensor self) |
static Tensor |
torch.tril_out(Tensor out,
Tensor self,
long diagonal) |
static Tensor |
torch.tril_outf(Tensor self,
long diagonal,
Tensor out) |
static Tensor |
torch.tril(Tensor self) |
static Tensor |
torch.tril(Tensor self,
long diagonal) |
static Tensor |
torch.triplet_margin_loss(Tensor anchor,
Tensor positive,
Tensor negative) |
static Tensor |
torch.triplet_margin_loss(Tensor anchor,
Tensor positive,
Tensor negative,
double margin,
double p,
double eps,
boolean swap,
long reduction) |
static Tensor |
torch.triplet_margin_loss(Tensor anchor,
Tensor positive,
Tensor negative,
double margin,
double p,
double eps,
boolean swap,
loss_reduction_t reduction) |
static Tensor |
torch.triplet_margin_loss(Tensor anchor,
Tensor positive,
Tensor negative,
TripletMarginLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.triplet_margin_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.triplet_margin_with_distance_loss(Tensor anchor,
Tensor positive,
Tensor negative) |
static Tensor |
torch.triplet_margin_with_distance_loss(Tensor anchor,
Tensor positive,
Tensor negative,
Pointer distance_function,
double margin,
boolean swap,
loss_reduction_t reduction) |
static Tensor |
torch.triplet_margin_with_distance_loss(Tensor anchor,
Tensor positive,
Tensor negative,
TripletMarginWithDistanceLossOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.triplet_margin_with_distance_loss
/** about the exact behavior of this functional.
|
static Tensor |
torch.triu_out(Tensor out,
Tensor self) |
static Tensor |
torch.triu_out(Tensor out,
Tensor self,
long diagonal) |
static Tensor |
torch.triu_outf(Tensor self,
long diagonal,
Tensor out) |
static Tensor |
torch.triu(Tensor self) |
static Tensor |
torch.triu(Tensor self,
long diagonal) |
static Tensor |
torch.true_divide_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.true_divide_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.true_divide(Tensor self,
Scalar other) |
static Tensor |
torch.true_divide(Tensor self,
Tensor other) |
static Tensor |
torch.trunc_(Tensor self) |
static Tensor |
torch.trunc_out(Tensor out,
Tensor self) |
static Tensor |
torch.trunc_outf(Tensor self,
Tensor out) |
static Tensor |
torch.trunc(Tensor self) |
static Node |
torch.try_get_grad_accumulator(Tensor arg0)
Attempts to get a pointer to the gradient accumulator of the
Variable,
if it still exists. |
static TensorVector |
torch.unbind(Tensor self) |
static TensorVector |
torch.unbind(Tensor self,
Dimname dim) |
static TensorVector |
torch.unbind(Tensor self,
long dim) |
static TensorVector |
torch.unflatten_dense_tensors(Tensor flat,
TensorArrayRef tensors) |
static Tensor |
torch.unfold_backward(Tensor grad_in,
long[] input_sizes,
long dim,
long size,
long step) |
static Tensor |
torch.unfold_backward(Tensor grad_in,
LongArrayRef input_sizes,
long dim,
long size,
long step) |
static Tensor |
torch.unfold(Tensor input,
LongPointer kernel_size,
LongPointer dilation,
LongPointer padding,
LongPointer stride) |
static Tensor |
torch.unfold(Tensor input,
UnfoldOptions options)
See https://pytorch.org/docs/master/nn.functional.html#torch.nn.functional.unfold
/** about the exact behavior of this functional.
|
static Tensor |
torch.uniform_(Tensor tensor) |
static Tensor |
torch.uniform_(Tensor tensor,
double low,
double high)
Fills the given 2-dimensional
matrix with values drawn from a uniform
distribution parameterized by low and high. |
static TensorTensorTensorTuple |
torch.unique_consecutive(Tensor self) |
static TensorTensorTensorTuple |
torch.unique_consecutive(Tensor self,
boolean return_inverse,
boolean return_counts,
LongOptional dim) |
static TensorTensorTensorTuple |
torch.unique_dim_consecutive(Tensor self,
long dim) |
static TensorTensorTensorTuple |
torch.unique_dim_consecutive(Tensor self,
long dim,
boolean return_inverse,
boolean return_counts) |
static TensorTensorTensorTuple |
torch.unique_dim(Tensor self,
long dim) |
static TensorTensorTensorTuple |
torch.unique_dim(Tensor self,
long dim,
boolean sorted,
boolean return_inverse,
boolean return_counts) |
static TensorVector |
torch.unsafe_chunk(Tensor self,
long chunks) |
static TensorVector |
torch.unsafe_chunk(Tensor self,
long chunks,
long dim) |
static TensorVector |
torch.unsafe_split_with_sizes(Tensor self,
long... split_sizes) |
static TensorVector |
torch.unsafe_split_with_sizes(Tensor self,
long[] split_sizes,
long dim) |
static TensorVector |
torch.unsafe_split_with_sizes(Tensor self,
LongArrayRef split_sizes) |
static TensorVector |
torch.unsafe_split_with_sizes(Tensor self,
LongArrayRef split_sizes,
long dim) |
static TensorVector |
torch.unsafe_split(Tensor self,
long split_size) |
static TensorVector |
torch.unsafe_split(Tensor self,
long split_size,
long dim) |
static Tensor |
torch.unsqueeze(Tensor self,
long dim) |
static Tensor |
torch.upsample_bicubic2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_bicubic2d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_bicubic2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_bicubic2d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d_outf(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_bicubic2d_outf(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_bicubic2d(Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d(Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bicubic2d(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bicubic2d(Tensor input,
LongArrayRefOptional output_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_bilinear2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_bilinear2d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_bilinear2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_bilinear2d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d_outf(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_bilinear2d_outf(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_bilinear2d(Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d(Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_bilinear2d(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_bilinear2d(Tensor input,
LongArrayRefOptional output_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_linear1d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales,
Tensor grad_input) |
static Tensor |
torch.upsample_linear1d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales,
Tensor grad_input) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_linear1d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_linear1d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d_outf(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales,
Tensor out) |
static Tensor |
torch.upsample_linear1d_outf(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales,
Tensor out) |
static Tensor |
torch.upsample_linear1d(Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d(Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_linear1d(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales) |
static Tensor |
torch.upsample_linear1d(Tensor input,
LongArrayRefOptional output_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest1d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest1d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest1d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest1d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest1d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest1d_out(Tensor out,
Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest1d_out(Tensor out,
Tensor self,
long[] output_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_out(Tensor out,
Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest1d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d_outf(Tensor self,
long[] output_size,
DoubleOptional scales,
Tensor out) |
static Tensor |
torch.upsample_nearest1d_outf(Tensor self,
LongArrayRef output_size,
DoubleOptional scales,
Tensor out) |
static Tensor |
torch.upsample_nearest1d(Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest1d(Tensor self,
long[] output_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest1d(Tensor self,
LongArrayRef output_size,
DoubleOptional scales) |
static Tensor |
torch.upsample_nearest1d(Tensor input,
LongArrayRefOptional output_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest2d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest2d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest2d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest2d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest2d_out(Tensor out,
Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest2d_out(Tensor out,
Tensor self,
long[] output_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_out(Tensor out,
Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest2d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d_outf(Tensor self,
long[] output_size,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_nearest2d_outf(Tensor self,
LongArrayRef output_size,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_nearest2d(Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest2d(Tensor self,
long[] output_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest2d(Tensor self,
LongArrayRef output_size,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest2d(Tensor input,
LongArrayRefOptional output_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest3d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest3d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest3d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest3d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
long[] output_size,
long... input_size) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest3d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_nearest3d_out(Tensor out,
Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest3d_out(Tensor out,
Tensor self,
long[] output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_out(Tensor out,
Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest3d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d_outf(Tensor self,
long[] output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_nearest3d_outf(Tensor self,
LongArrayRef output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_nearest3d(Tensor self,
long... output_size) |
static Tensor |
torch.upsample_nearest3d(Tensor self,
long[] output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d(Tensor self,
LongArrayRef output_size) |
static Tensor |
torch.upsample_nearest3d(Tensor self,
LongArrayRef output_size,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_nearest3d(Tensor input,
LongArrayRefOptional output_size,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_trilinear3d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_backward_out(Tensor grad_input,
Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_backward_out(Tensor grad_input,
Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_backward_outf(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_trilinear3d_backward_outf(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor grad_input) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
long[] output_size,
long[] input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
LongArrayRef output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
long[] input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_trilinear3d_backward(Tensor grad_output,
LongArrayRefOptional output_size,
LongArrayRef input_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.upsample_trilinear3d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_out(Tensor out,
Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d_out(Tensor out,
Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d_outf(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_trilinear3d_outf(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w,
Tensor out) |
static Tensor |
torch.upsample_trilinear3d(Tensor self,
long[] output_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d(Tensor self,
long[] output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d(Tensor self,
LongArrayRef output_size,
boolean align_corners) |
static Tensor |
torch.upsample_trilinear3d(Tensor self,
LongArrayRef output_size,
boolean align_corners,
DoubleOptional scales_d,
DoubleOptional scales_h,
DoubleOptional scales_w) |
static Tensor |
torch.upsample_trilinear3d(Tensor input,
LongArrayRefOptional output_size,
boolean align_corners,
DoubleArrayRefOptional scale_factors) |
static Tensor |
torch.value_selecting_reduction_backward(Tensor grad,
long dim,
Tensor indices,
long[] sizes,
boolean keepdim) |
static Tensor |
torch.value_selecting_reduction_backward(Tensor grad,
long dim,
Tensor indices,
LongArrayRef sizes,
boolean keepdim) |
static Tensor |
torch.vander(Tensor x) |
static Tensor |
torch.vander(Tensor x,
LongOptional N,
boolean increasing) |
static TensorTensorTuple |
torch.var_mean(Tensor self) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
boolean unbiased) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
DimnameArrayRef dim) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
DimnameArrayRef dim,
LongOptional correction) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
int dim) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
long... dim) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
LongArrayRef dim) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
LongArrayRefOptional dim,
LongOptional correction) |
static TensorTensorTuple |
torch.var_mean(Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
LongOptional correction) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
long... dim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
LongArrayRef dim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
LongArrayRefOptional dim,
LongOptional correction) |
static Tensor |
torch.var_out(Tensor out,
Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.var_outf(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.var_outf(Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim,
Tensor out) |
static Tensor |
torch.var_outf(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.var_outf(Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim,
Tensor out) |
static Tensor |
torch.var_outf(Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim,
Tensor out) |
static Tensor |
torch.var(Tensor self) |
static Tensor |
torch.var(Tensor self,
boolean unbiased) |
static Tensor |
torch.var(Tensor self,
DimnameArrayRef dim) |
static Tensor |
torch.var(Tensor self,
DimnameArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var(Tensor self,
DimnameArrayRef dim,
LongOptional correction) |
static Tensor |
torch.var(Tensor self,
DimnameArrayRef dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.var(Tensor self,
int dim) |
static Tensor |
torch.var(Tensor self,
long... dim) |
static Tensor |
torch.var(Tensor self,
long[] dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var(Tensor self,
LongArrayRef dim) |
static Tensor |
torch.var(Tensor self,
LongArrayRef dim,
boolean unbiased,
boolean keepdim) |
static Tensor |
torch.var(Tensor self,
LongArrayRefOptional dim,
LongOptional correction) |
static Tensor |
torch.var(Tensor self,
LongArrayRefOptional dim,
LongOptional correction,
boolean keepdim) |
static Tensor |
torch.vdot_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.vdot_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.vdot(Tensor self,
Tensor other) |
static void |
torch.vector_to_parameters(Tensor vec,
Tensor parameters) |
static VariableVersion |
torch.version_counter(Tensor arg0)
Retrieves this
Variables version counter. |
static Tensor |
torch.view_as_complex(Tensor self) |
static Tensor |
torch.view_as_real(Tensor self) |
static TensorVector |
torch.vsplit(Tensor self,
long... indices) |
static TensorVector |
torch.vsplit(Tensor self,
long sections) |
static TensorVector |
torch.vsplit(Tensor self,
LongArrayRef indices) |
static Tensor |
torch.vstack_out(Tensor out,
TensorArrayRef tensors) |
static Tensor |
torch.vstack_outf(TensorArrayRef tensors,
Tensor out) |
static TensorVector |
torch.where(Tensor condition) |
static Tensor |
torch.where(Tensor condition,
Scalar self,
Scalar other) |
static Tensor |
torch.where(Tensor condition,
Scalar self,
Tensor other) |
static Tensor |
torch.where(Tensor condition,
Tensor self,
Scalar other) |
static Tensor |
torch.where(Tensor condition,
Tensor self,
Tensor other) |
static Tensor |
torch.xavier_normal_(Tensor tensor) |
static Tensor |
torch.xavier_normal_(Tensor tensor,
double gain)
Fills the input
Tensor with values according to the method
described in "Understanding the difficulty of training deep feedforward
neural networks" - Glorot, X. |
static Tensor |
torch.xavier_uniform_(Tensor tensor) |
static Tensor |
torch.xavier_uniform_(Tensor tensor,
double gain)
Fills the input
Tensor with values according to the method
described in "Understanding the difficulty of training deep feedforward
neural networks" - Glorot, X. |
static Tensor |
torch.xlogy_(Tensor self,
Scalar other) |
static Tensor |
torch.xlogy_(Tensor self,
Tensor other) |
static Tensor |
torch.xlogy_out(Tensor out,
Scalar self,
Tensor other) |
static Tensor |
torch.xlogy_out(Tensor out,
Tensor self,
Scalar other) |
static Tensor |
torch.xlogy_out(Tensor out,
Tensor self,
Tensor other) |
static Tensor |
torch.xlogy_outf(Scalar self,
Tensor other,
Tensor out) |
static Tensor |
torch.xlogy_outf(Tensor self,
Scalar other,
Tensor out) |
static Tensor |
torch.xlogy_outf(Tensor self,
Tensor other,
Tensor out) |
static Tensor |
torch.xlogy(Scalar self,
Tensor other) |
static Tensor |
torch.xlogy(Tensor self,
Scalar other) |
static Tensor |
torch.xlogy(Tensor self,
Tensor other) |
static Tensor |
torch.xor(Scalar x,
Tensor y) |
static Tensor |
torch.xor(Tensor x,
Scalar y) |
static Tensor |
torch.xor(Tensor x,
Tensor y) |
static Tensor |
torch.zero_(Tensor self) |
static Tensor |
torch.zeros_(Tensor tensor)
Fills the given
tensor with zeros. |
static Tensor |
torch.zeros_like(Tensor self) |
static Tensor |
torch.zeros_like(Tensor self,
ScalarTypeOptional dtype,
LayoutOptional layout,
DeviceOptional device,
BoolOptional pin_memory,
MemoryFormatOptional memory_format) |
static Tensor |
torch.zeros_like(Tensor self,
TensorOptions options,
MemoryFormatOptional memory_format) |
static Tensor |
torch.zeros_out(Tensor out,
long... size) |
static Tensor |
torch.zeros_out(Tensor out,
LongArrayRef size) |
static Tensor |
torch.zeros_outf(long[] size,
Tensor out) |
static Tensor |
torch.zeros_outf(LongArrayRef size,
Tensor out) |
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